If you’re unfamiliar with the term, decentralized finance (DeFi) is a subset of blockchain technology that focuses on financial applications powered by distributed ledgers. In essence, DeFi represents the next generation of financial services, where individual users have more control and transparency over their finances.

Over the years, DeFi has changed the way we think about money, and with some of the key benefits of using DeFi, there are many possibilities this technology holds for the future.

Also read: Potential Use Cases of Blockchain Technology for Cybersecurity

What is DeFi?

DeFi is a term used for Ethereum and other blockchain applications that allow for a peer-to-peer transaction without needing an intermediary such as a bank, central bank, or other financial institution. Because there is no central authority, all transactions are visible to everyone involved, providing more transparency and accountability.

In addition, DeFi applications tend to be more flexible and faster than traditional centralized systems, which can often be bogged down by bureaucracy. Moreover, users have direct control over their own funds in a DeFi system, meaning they can decide how to use their money without going through a third party.

While DeFi still has some associated risks, the potential benefits make it an appealing option for those looking for alternatives to traditional financial systems.

Current State and Potential of DeFi

In 2021, some outlets reported that DeFi’s growth on the Ethereum blockchain was 780% year-over-year. By the first quarter of 2022, the total value locked (TVL) in DeFi protocols was over $172 billion.

The current state of DeFi is characterized by four key trends: composability, yield farming, DeFi insurance, and governance.

Composability

Composability refers to the ability of different components to work together to achieve the desired outcome. In the context of DeFi, composability refers to the ability of different protocols and platforms to interoperate to create new financial applications and products.

This interoperability is made possible by using open standards and APIs (application programming interfaces), allowing developers to build on existing infrastructure rather than starting from scratch.

This isn’t to say that composability hasn’t existed in traditional finance. For example, when you use PayPal to buy something on Amazon or pay for an Uber, you use two different platforms that can work together. However, DeFi takes composability to the next level by making it possible to create a trustless system.

Every transaction and activity is verifiable on the blockchain. Ethereum is the neutral settlement layer, and no single entity wields power. In addition, the permissionless nature of DeFi means anyone can create new financial products and applications that wouldn’t be possible with traditional infrastructure.

As more protocols and platforms begin to interoperate with each other, we can expect an exponential increase in the number and variety of available DeFi applications and products.

Yield Farming

Yield farming is the practice of staking cryptocurrencies to earn rewards. This can be done by providing liquidity to various exchanges or participating in staking pools.

Yield farmers typically use multiple protocols to maximize their rewards. Due to the high risk involved in yield farming, many farmers diversify their portfolios across multiple projects.

Yield farming generally offers higher rewards than traditional staking, but it is also a more volatile practice. Therefore, yield farmers must carefully monitor the price of the tokens they are staking to avoid losses. Additionally, they must be aware of rug pulls, smart contract hacks, and other risks associated with yield farming.

Yield farming has become a popular way to earn cryptocurrency rewards despite the risks. However, it remains to be seen whether this practice is sustainable in the long term.

DeFi insurance

DeFi insurance is the missing piece to bring DeFi to par with traditional finance.

DeFi insurance has arisen out of necessity, as evidenced by the estimated $10 billion lost in the DeFi industry to fraud in 2021. Insurance protects against adverse events in the space, such as exchange hacks, smart contract failures, and stablecoin price crashes. Anyone can provide DeFi insurance by joining a pool.

In addition to the aforementioned coverage, other possibilities for DeFi insurance include DvP (delivery versus payment) protocols and flash loans. However, despite the advantages offered by DeFi insurance, the claims process is still uncertain. Consequently, more research is needed to assess the effectiveness of this new tool.

See Blockchain Hackers Cost Crypto Ecosystems More Than $1B in Q1 2022

Governance

Several DeFi platforms are resoundingly reaffirming the blockchain community’s dedication to decentralization by making governance tokens available to users.

A governance token grants users a certain amount of power over the platform’s protocol, products, and future features. Governance tokens are frequently created using decentralized protocols that encourage community-driven development and self-sustainability.

Decentralized networking projects require governance techniques to make critical decisions about protocol modifications, recruitment, and even governance framework adjustments.

For example, a borrowing and lending platform may utilize its governing procedure to calculate the required amount. In other words, the decisions made by a project’s stakeholders through its governing system can directly impact its success or failure.

With the right approach, governance initiatives have the potential to usher in a new era of decentralized development and cooperation.

Also read: Top 5 Benefits of AI in Banking and Finance

Challenges of DeFi

As the DeFi sector has grown, one key challenge is ensuring the playing field is level for all market participants, regardless of their size or location. Another is the need for stronger global regulatory coordination to prevent DeFi protocols from being used for illicit purposes. Finally, as DeFi protocols continue to evolve and mature, there is a need to develop more robust governance mechanisms to ensure they can adapt and respond to changing conditions.

While the challenges facing DeFi are significant, so too are the rewards. With its ability to empower individuals and communities worldwide with greater access to financial services, DeFi represents a vital step forward in achieving financial inclusion for all.

Future of DeFi

The DeFi space is still in its early stages, and it remains to be seen what the future holds. However, with its ability to reduce barriers to entry, increase access to financial services, and enable more democratic governance structures, DeFi has the potential to reshape the future of finance for the better.

Near instant and secure transactions is a critical area to watch. With traditional finance, transactions can take days or even weeks to clear. This is not the case with DeFi. Due to the decentralized nature of the sector, transactions are settled almost instantly, making it ideal for activities such as trading or lending, where time is of the essence.

Easier borrowing and lending are inevitable with DeFi. In the traditional financial system, it can be challenging to get access to loans because banks and other financial institutions are often reluctant to lend to individuals with no collateral. However, in the DeFi space, you can use your crypto assets as collateral for a loan. This opens up access to credit for many people who would otherwise be financially excluded.

Cross-communication and the ability to exchange assets are other areas of interest. In traditional finance, there are often silos between different asset classes. For example, you might have a bank account for your savings, a brokerage account for your stocks and shares, and a pension for your retirement savings. However, new DeFi applications allow users to easily trade between different asset classes without going through a centralized exchange. This increases efficiency and reduces costs.

Honesty and trust are two values that are important in any financial system. Unfortunately, they are often lacking in traditional finance. For example, banks have been known to mis-sell products to customers or charge hidden fees. However, in the DeFi space, everything is out in the open and transparent. This helps to build trust between users and developers and creates a more open financial system overall.

All in all, there are many reasons why DeFi could reshape the future of finance for the better.

The post How DeFi is Reshaping the Future of Finance appeared first on IT Business Edge.

As a result, companies have been looking for ways to speed up the process. One of the solutions that has recently emerged is low-code and no-code (LCNC) development tools. These tools allow users to create applications without writing much if any code.

Low-code development has become increasingly popular in recent years, and Gartner predicts that it will account for over two-thirds of application development by 2024. A study by Statista has also found that low/no-code development tool spending will grow from just under $13 billion in 2020 to $65 billion by 2027.

In the same way tools like Canva and Visme have empowered a new generation of graphic designers, no-code and low-code platforms are giving rise to a new breed of citizen developers. These people with little or no coding experience are using these tools to build working applications.

There are many reasons why software engineering is moving in this direction. Below, we discuss a few reasons the LCNC approach is the key to faster engineering.

Also read: Democratizing Software Development with Low-Code

What are Low-Code and No-Code Tools?

First, it’s important to distinguish between no-code and low-code platforms. No-code platforms allow users to create working applications without writing any code. They are becoming popular with business users because they enable them to solve problems independently and optimize day-to-day processes without waiting for IT to do it.

On the other hand, low-code platforms require some coding but aim to make the process easier with drag-and-drop interfaces and prebuilt components. They are aimed at professional developers and allow them to build applications faster by automating some of the more tedious tasks involved in coding, such as creating boilerplate code or scaffolding.

How Low/No-Code Accelerates Software Engineering

There are several reasons why LCNC is the key to faster engineering.

Ease of use

One of the biggest advantages of LCNC development platforms is they are much easier to use than traditional coding environments. This is because they provide a graphical user interface (GUI) that allows users to drag and drop components to build applications. No-code platforms take this a step further by not requiring any coding.

This ease of use means users don’t need to be skilled programmers to build an application. It opens up the possibility for anyone to create working applications without any coding experience.

Speed

LCNC development is a powerful tool for software engineering teams. It speeds up the development process by allowing developers to create sophisticated applications visually. Users can accelerate requirements gathering, prototype faster, and save time on wireframes and complex coding. In addition, LCNC development tools often come with prebuilt libraries of code, which can further speed up the development process.

Agile iteration

The ability to quickly experiment and test new ideas is essential to maintaining a competitive edge. Open-source, low-code development platforms enable web developers to prototype and deploy new applications with minimal effort rapidly.

There is no need for lengthy development cycles or complex code; developers can add new features quickly and easily. This makes it possible to experiment with new ideas and get feedback from users rapidly, making it easier to improve upon them.

Easy data integration

Developers can quickly and easily build applications that connect to, work with, and consolidate data from various sources. This means they can spend less time worrying about the technical details of data integration and more time focusing on building great applications.

Lower costs and easier scalability

Another advantage of using a LCNC development platform is that it can save money. No-code platforms, in particular, have the potential to reduce development costs by allowing businesses to build applications without having to hire expensive developers.

In addition, LCNC platforms are often much easier to scale than traditional coding environments. This is because they are designed to be modular, so users can add new features quickly and easily.

Mobile experiences optimization

LCNC development platforms make it easy to optimize applications for mobile devices. For example, they allow developers to create responsive designs that automatically adapt to any screen size.

Thus, users can quickly and easily create applications that look great on any device without worrying about coding for specific devices.

Better application life cycle management

LCNC development platforms often come with built-in tools for managing the life cycle of applications. This includes features such as version control to keep track of changes to code and collaboration tools to work with other developers on the team.

This makes it easier to manage the development process and ensure applications are always up-to-date.

SaaS integration without programming

Low-code development is often associated with app creation, but it can be useful for much more. Low-code platforms offer an easy way to connect data and operations, making them ideal for integrating with software-as-a-service (SaaS) applications. This is especially important for businesses that rely on customer relationship management (CRM) or marketing solutions. With a low-code platform, users can quickly and easily connect applications to the tools needed without spending hours coding custom integrations.

Also read: Effectively Using Low-Code/No-Code in the Developer Cycle

Limitations of Low-Code Platforms

We would be remiss if we failed to mention some of the limitations of low-code platforms.

Limited capability for complexity

One limitation is that low-code platforms are inadequate for complex applications. This is because they often lack the flexibility of traditional coding environments. They are typically suitable for customer-facing applications, web and mobile front ends, and business process or workflow applications but are not ideal for infrastructure deployment, back-end APIs (application programming interfaces), and intensive customization.

Many tools are not enterprise-grade

Another limitation is that low-code platforms are not always suitable for enterprise-grade applications. This is because they often lack the security and scalability features required for large-scale applications.

Getting Started With Low-Code and No-Code Development

Despite these limitations, all indications are that these tools will keep getting better and better. And as they do, they will become more and more popular. So, if you’re looking to start low-code development, now is the time.

There are a few things you should keep in mind when getting started, such as:

• What type of application do you want to build?
• What is your budget?
• How much time do you have to build your application?
• What is your level of coding experience?

If you can answer these questions, you’ll be well on your way to finding the right LCNC platform for your needs. However, if you’re unsure where to start, check out this low-code cheat sheet.

Read next: 10 User-Centered Software Design Mistakes to Avoid

The post Why Low-Code/No-Code is the Key to Faster Engineering appeared first on IT Business Edge.

In this introductory tutorial, you’ll learn the basics of Python for machine learning, including different model types and the steps to take to ensure you obtain quality data, using a sample machine learning problem. In addition, you’ll get to know some of the most popular libraries and tools for machine learning.

Jump to:

• Machine Learning 101
• Python Libraries and Tools
• Training a Machine Learning Algorithm with Python Using the Iris Flowers Dataset

Also read: Best Machine Learning Software

Machine Learning 101

Machine learning (ML) is a form of artificial intelligence (AI) that teaches computers to make predictions and recommendations and solve problems based on data. Its problem-solving capabilities make it a useful tool in industries such as financial services, healthcare, marketing and sales, and education among others.

Types of machine learning

There are three main types of machine learning: supervised, unsupervised, and reinforcement.

Supervised learning

In supervised learning, the computer is given a set of training data that includes both the input data (what we want to predict) and the output data (the prediction). The computer then learns a model that maps input to output data to make predictions on new, unseen data.

Unsupervised learning

In unsupervised learning, the computer is only given the input data. The computer then learns to find patterns and relationships in the data and applies this to things like clustering or dimensionality reduction.

You can use many different algorithms for machine learning. Some popular examples include:

• Linear regression
• Logistic regression
• Decision trees
• Random forests
• Support vector machines
• Naive bayes
• Neural networks

The choice of algorithm will depend on the problem you are trying to solve and the available data.

Reinforcement learning

Reinforcement learning is a process where the computer learns by trial and error. The computer is given a set of rules (the environment) and must learn how to maximize its reward (the goal). This can be used for things like playing games or controlling robots.

The steps of a machine learning project

Data import

The first step in any machine learning project is to import the data. This data can come from various sources, including files on your computer, databases, or web APIs. The format of the data will also vary depending on the source.

For example, you may have a CSV file containing tabular data or an image file containing raw pixel data. No matter the source or format, you must load the data into memory before doing anything with it. This can be accomplished using a library like NumPy, Scikit Learn, or Pandas.

Once the data is loaded, you will usually want to scrutinize it to ensure everything looks as expected. This step is critical, especially when working with cluttered or unstructured data.

Data cleanup

Once you have imported the data, the next step is to clean it up. This can involve various tasks, such as removing invalid, missing, or duplicated data; converting data into the correct format; and normalizing data. This step is crucial because it can make a big difference in the performance of your machine learning model.

For example, if you are working with tabular data, you will want to ensure all of the columns are in the proper format (e.g., numeric values instead of strings). You will also want to check missing values and decide how to handle them (e.g., imputing the mean or median value).

If you are working with images, you may need to resize or crop them to be the same size. You may also want to convert images from RGB to grayscale.

Also read: Top Data Quality Tools & Software

Splitting data into training/test sets

After cleaning the data, you’ll need to split it into training and test sets. The training set is used to train the machine learning model, while the test set evaluates the model. Keeping the two sets separate is vital because you don’t want to train the model on the test data. This would give the model an unfair advantage and likely lead to overfitting.

A standard split for large datasets is 80/20, where 80% of the data is used for training and 20% for testing.

Model creation

Using the prepared data, you’ll then create the machine learning model. There are a variety of algorithms you can use for this task, but determining which to use depends on the goal you wish to achieve and the existing data.

For example, if you are working with a small dataset, you may want to use a simple algorithm like linear regression. If you are working with a large dataset, you may want to use a more complex algorithm like a neural network.

In addition, decision trees may be ideal for problems where you need to make a series of decisions. And random forests are suitable for problems where you need to make predictions based on data that is not linearly separable.

Model training

Once you have chosen an algorithm and created the model, you need to train it on the training data. You can do this by passing the training data through the model and adjusting the parameters until the model learns to make accurate predictions on the training data.

For example, if you train a model to identify images of cats, you will need to show it many photos of cats labeled as such, so it can learn to recognize them.

Training a machine learning model can be pretty complex and is often an iterative process. You may also need to try different algorithms, parameter values, or ways of preprocessing the data.

Evaluation and improvement

After you train the model, you’ll need to evaluate it on the test data. This step will give you a good indication of how well the model will perform on unseen data.

If the model does not perform well on the test data, you will need to go back and make changes to the model or the data. This is often the usual scenario when you first train a model—you must go back and iterate several times until you get a model that performs well.

This process is known as model tuning and is an integral part of the machine learning workflow.

Also read: Top 7 Trends in Software Product Design for 2022

Python Libraries and Tools

There are several libraries and tools that you can use to build machine learning models in Python.

Scikit-learn

One of the most popular libraries is scikit-learn. It features various classification, regression, and clustering algorithms, including support vector machines, random forests, gradient boosting, k-means, and DBSCAN.

The library is built on NumPy, SciPy, and Matplotlib libraries. In addition, it includes many utility functions for data preprocessing, feature selection, model evaluation, and input/output.

Scikit-learn is one of the most popular machine learning libraries available today, and you can use it for various tasks. For example, you can use it to build predictive models for classification or regression problems. You can also use it for unsupervised learning tasks such as clustering or dimensionality reduction.

NumPy

NumPy is another popular Python library that supports large, multi-dimensional arrays and matrices. It also includes several routines for linear algebra, Fourier transform, and random number generation.

NumPy is widely used in scientific computing and has become a standard tool for machine learning problems.

Its popularity is due to its ease of use and efficiency; NumPy code is often much shorter and faster than equivalent code written in other languages. In addition, NumPy integrates well with other Python libraries, making it easy to use in a complete machine learning stack.

Pandas

Pandas is a powerful Python library for data analysis and manipulation. It’s commonly used in machine learning applications for preprocessing data, as it offers a wide range of features for cleaning, transforming, and manipulating data. In addition, Pandas integrates well with other scientific Python libraries, such as NumPy and SciPy, making it a popular choice for data scientists and engineers.

At its core, Pandas is designed to make working with tabular data easier. It includes convenient functions for reading in data from various file formats; performing basic operations on data frames, such as selection, filtering, and aggregation; and visualizing data using built-in plotting functions. Pandas also offers more advanced features for dealing with complex datasets, such as join/merge operations and time series manipulation.

Pandas is a valuable tool for any data scientist or engineer who needs to work with tabular data. It’s easy to use and efficient, and it integrates well with other Python libraries.

Matplotlib

Matplotlib is a Python library that enables users to create two-dimensional graphics. The library is widely used in machine learning due to its ability to create visualizations of data. This is valuable for machine learning problems because it allows users to see patterns in the data that they may not be able to discern by looking at raw numbers.

Additionally, you can use Matplotlib to create simulations of machine learning algorithms. This feature can be helpful for debugging purposes or for understanding how the algorithm works.

Seaborn

Seaborn is a Python library for creating statistical graphics. It’s built on top of Matplotlib and integrates well with Pandas data structures.

Seaborn is often used for exploratory data analysis, as it allows you to create visualizations of your data easily. In addition, you can use Seaborn to create more sophisticated visualizations, such as heatmaps and time series plots.

Overall, Seaborn is a valuable tool for any data scientist or engineer who needs to create statistical graphics.

Jupyter Notebook

The Jupyter Notebook is a web-based interactive programming environment that allows users to write and execute code in various languages, including Python.

The Notebook has gained popularity in the machine learning community due to its ability to streamline the development process by allowing users to write and execute code in the same environment and inspect the data frequently.

Another reason for its popularity is its graphical user interface (GUI), which makes it easier to use than command-line editors such as Terminal and VS Code. For example, it isn’t easy to visualize and inspect data that contains several columns in a command-line editor.

Training a Machine Learning Algorithm with Python Using the Iris Flowers Dataset

For this example, we will be using the Jupyter Notebook to train a machine learning algorithm with the classic Iris Flowers dataset.

Although the Iris Flowers dataset is small, it will allow us to demonstrate how to use Python for machine learning. This dataset has been used extensively in pattern recognition and machine learning literature. It is also relatively easy to understand, making it a good choice for our first problem.

The Iris Flowers dataset contains 150 observations of Iris flowers. The goal is to take measurements of flowers and use that data to predict what species of Iris it is based on the following physical parameters of three Iris species:

• Versicolor
• Setosa
• Virginica

Installing Jupyter Notebook with Anaconda

Before getting started with training the machine learning algorithm, we will need to install Jupyter. To do so, we will use a platform known as Anaconda.

Anaconda is a free and open-source distribution of the Python programming language that includes the Jupyter Notebook. It also has various other useful libraries for data analysis, scientific computing, and machine learning. 

Jupyter Notebook with Anaconda is a powerful tool for any data scientist or engineer working with Python, whether using Windows, Mac, or Linux operating systems (OSs).

Visit the Anaconda website and download the installer for your operating system. Follow the instructions to install it, and launch the Anaconda Navigator application.

To do this on most OSs, you must open a terminal window, type jupyter notebook, and hit Enter. This action will start the Jupyter Notebook server on your machine.

It also automatically displays the Jupyter Dashboard in a new browser window pointing to your Localhost at port 8888.

Creating a new notebook

Once you have Jupyter installed, you can begin training your machine learning algorithm. Start by creating a new notebook.

To create a new notebook, select the folder where you want to store the new notebook and then click the New button in the upper right corner of the interface and select Python [default]. This action will create a new notebook with Python code cells.

New notebooks are automatically opened in a new browser tab named Untitled. You can rename it by clicking Untitled. For our tutorial, rename it Iris Flower.

Importing a dataset into Jupyter

We’ll get our dataset from the Kaggle website. Head over to Kaggle.com and create a free account using a custom email, Google, or Facebook.

Next, find the Iris dataset by clicking Datasets in the left navigation pane and entering Iris Flowers in the search bar.

The CSV file contains 150 records under five attributes—petal length, petal width, sepal length, sepal width, and class (species)—so there are only five columns in total.

Once you’ve found the dataset, click the Download button, and ensure the download location is the same as that of your Jupyter Notebook. Unzip the file to your computer.

Next, open Jupyter Notebook and click on the Upload button in the top navigation bar. Find the dataset on your computer and click Open. You will now upload the dataset to your Jupyter Notebook environment.

Data preparation

We can now import the dataset into our program. We’ll use the Pandas library for this. This pre-prepared dataset doesn’t have much to do with data preparation.

Start by typing the following code into a new cell and click run:

import pandas as pd

iris=pd.read_csv(‘Iris.csv’)

iris

This first line will import the Pandas library into our program, allow us to use it, and rename it pd.

The second line will read the CSV file and store it in a variable called iris. View the dataset by typing iris and running the cell.

You should see something similar to the image below:

As you can see, each row represents one Iris flower with its attributes listed in the columns.

The first four columns are the attributes or features of the Iris flower, and the last column is the class label which corresponds to a species of Iris Flower, such as Iris setosa, Iris virginica, etc.

Before proceeding, we need to remove the ID column because it can cause problems with our classification model. To do so, enter the following code in a new cell.

iris.drop(columns = ‘Id’, inplace = True)

Type iris once more to see the output. You will notice the Id column has been dropped.

Understanding the Data

Now that we know how to import the dataset let’s look at some basic operations we can perform to understand the data better.

First, let’s see what data types are in our dataset. To do this, we’ll use the dtypes attribute of the dataframe object. Type the following code into a new cell and run it:

iris.dtypes

You should see something like this:

You can see that all of the columns are floats except for the Species column, which is an object. This is because objects in Pandas are usually strings.

Now let’s examine some summary statistics for our data using the describe function. Type the following code into a new cell and run it:

iris.describe

You can see that this gives us some summary statistics for each column in our dataset.

We can also use the head and tail functions to look at the first and last few rows of our dataset, respectively. Type the following code into a new cell and run it:

iris.head()

Then type:

iris.tail()

We can see the first five rows of our dataframe correspond to the Iris setosa class, and the last five rows correspond to the Iris virginica.

Next, we can visualize the data using several methods. For this, we will need to import two libraries, Matplotlib and Seaborn.

Type the following code into a new cell:

import seaborn as sns

import matplotlib.pyplot as plt

You will also need to set the style and color codes of Seaborn. Additionally, the current Seaborn version generates warnings that we can ignore for this tutorial. Enter the following code:

sns.set(style=”white”, color_codes=True)

import warnings

warnings.filterwarnings(“ignore”)

For the first visualization, create a scatter plot using Matplotlib. Enter the following code in a new cell.

iris.plot(kind=”scatter”, x=”SepalLengthCm”, y=”SepalWidthCm”)

This will generate the following output:

However, to color the scatterplot by species, we will use Seaborn’s FacetGrid class. Enter the following code in a new cell.

sns.FacetGrid(iris, hue=”Species”, size=5) \

  .map(plt.scatter, “SepalLengthCm”, “SepalWidthCm”) \

  .add_legend()

Your output should be as follows:

As you can see, Seaborn has automatically colored our scatterplot, so we can visualize our dataset better and see differences in sepal width and length for the three different Isis species.

We can also create a boxplot using Seaborn to visualize the petal length of each species. Enter the following code in a new cell:

sns.boxplot(x=”Species”, y=”PetalLengthCm”, data=iris)

You can also extend this plot by adding a layer of individual points using Seaborn’s striplot. Type the following code in a new cell:

ax = sns.boxplot(x=”Species”, y=”PetalLengthCm”, data=iris)

ax = sns.stripplot(x=”Species”, y=”PetalLengthCm”, data=iris, jitter=True, edgecolor=”gray”)

Another possible visualization is the kernel density plots (KD Plots) which shows the probability density. Enter the following code:

sns.FacetGrid(iris, hue=”Species”, size=6) \

  .map(sns.kdeplot, “PetalLengthCm”) \

  .add_legend()

A Pairplot is another useful Seaborn visualization. It shows the relationships between all columns in our dataset. Enter the following code into a new cell:

sns.pairplot (iris, hue=”Species”, size=3)

The output should be as follows:

From the above, you can quickly tell the Iris setosa species is separated from the rest across all feature combinations.

Similarly, you can also create a Boxplot grid using the code:

iris.boxplot(by=”Species”, figsize=(12, 6))

Let’s perform one final visualization that places each feature on a 2D plane. Enter the code:

from pandas.plotting import radviz

radviz(iris, “Species”)

Split the data into a test and training set

Having understood the data, you can now proceed and begin training the model. But first we need to split our data into a training and test set. To do this, we will use a function known as train_test_split from the scikit-learn library. This action will divide our data set into a ratio of 70:30 (Our dataset is small hence a higher test set).

Enter the following code in a new cell:

from sklearn.metrics import confusion_matrix

from sklearn.metrics import classification_report

from sklearn.model_selection import train_test_split

Next, separate the data into dependent and independent variables:

X = iris.iloc[:, :-1].values

y = iris.iloc[:, -1].values

Split into a training and test set:

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3)

The confusion matrix we imported is a table that is often used to evaluate the performance of a machine learning algorithm. The matrix comprises four quadrants, each representing the predicted and actual values for one of the two classes.

The first quadrant represents the true positives, or the observations correctly predicted to be positive. The second quadrant represents the false positives, which are the observations that were incorrectly predicted to be positive. The third quadrant represents the false negatives, which are the observations that were incorrectly predicted to be negative. Finally, the fourth quadrant represents the true negatives, or the observations correctly predicted to be negative.

The matrix rows represent the actual values, while the columns represent the predicted values.

Train the model and check accuracy

We will train the model and check the accuracy using four different algorithms: logistic regression, random forest classifier, decision tree classifier, and multinomial naive bayes.

To do so, we will create a series of objects in various classes and store them in variables. Be sure to take note of the accuracy scores.

Logistic regression

Enter the code below in a new cell:

from sklearn.linear_model import LogisticRegression

classifier = LogisticRegression()

classifier.fit(X_train, y_train)

y_pred = classifier.predict(X_test)

print(classification_report(y_test, y_pred))

print(confusion_matrix(y_test, y_pred))

from sklearn.metrics import accuracy_score

print(‘accuracy is’,accuracy_score(y_pred,y_test))

Random forest classifier

Enter the code below in a new cell:

from sklearn.ensemble import RandomForestClassifier

classifier=RandomForestClassifier(n_estimators=100)

classifier.fit(X_train, y_train)

y_pred = classifier.predict(X_test)

print(classification_report(y_test, y_pred))

print(confusion_matrix(y_test, y_pred))

print(‘accuracy is’,accuracy_score(y_pred,y_test))

Decision tree classifier

Enter the code below in a new cell:

from sklearn.tree import DecisionTreeClassifier

classifier = DecisionTreeClassifier()

classifier.fit(X_train, y_train)

y_pred = classifier.predict(X_test)

print(classification_report(y_test, y_pred))

print(confusion_matrix(y_test, y_pred))

print(‘accuracy is’,accuracy_score(y_pred,y_test))

Multinomial naive bayes

Enter the following code in a new cell:

from sklearn.naive_bayes import MultinomialNB

classifier = MultinomialNB()

classifier.fit(X_train, y_train)

y_pred = classifier.predict(X_test)

print(classification_report(y_test, y_pred))

print(confusion_matrix(y_test, y_pred))

print(‘accuracy is’,accuracy_score(y_pred,y_test))

Evaluating the model

Based on the training, we can see that three of our four algorithms have a high accuracy of 0.97. We can therefore choose any of these to evaluate our model. For this tutorial, we have selected the decision tree, which has high accuracy.

We will give our model sample values for sepal length, sepal width, petal length, and petal width and ask it to predict which species it is.

Our sample flower has the following dimensions in centimeters (cms):

• Sepal length: 6
• Sepal width: 3
• Petal length: 4
• Petal width: 2

Using a decision tree, enter the following code:

predictions = classifier.predict([[6,3,4,2]])

classifier.predict([[6,3,4,2]])

The output result is Iris-virginica.

Some Final Notes

As an introductory tutorial, we used the Iris Flowers dataset, which is a straightforward dataset containing only 150 records. Our training set only has 45 records (30%), hence similar accuracies with most of the algorithms.

However, in a real-world situation, the dataset may have thousands or millions of records. That said, Python is well-suited for handling large datasets and can easily scale up to higher dimensions.

Read next: Kubernetes: A Developers Best Practices Guide

The post Python for Machine Learning: A Tutorial appeared first on IT Business Edge.

It is an essential capability for most enterprises today because it underpins important services such as:

• Financial transactions
• Fraud prevention
• Identity management
• Social media monitoring
• Recommendation engines
• Customer relationship management (CRM)

In addition, predictive analytics, artificial intelligence (AI), and machine learning (ML) are all premised on a well-designed and functioning real-time data processing system.

As businesses have begun to adapt to the challenges of the current data landscape, more have developed an approach toward shaping their organization with real-time data processing.

See the Top Artificial Intelligence (AI) Software for 2022

Challenges with the Current Data Processing Landscape

The data processing landscape has changed dramatically in recent years. Previously, data centers processed data in batches; it was collected over time and then processed all at once.

This approach works well when data isn’t time-sensitive, but real-time data processing has become essential for many organizations as business needs have shifted and data has become more complex.

Currently, the biggest challenge is scaling. Enterprises need to scale real-time resources cost-effectively while simultaneously increasing revenue. Unfortunately, several issues make this difficult.

Massive data growth

The rise of big data has made scaling a challenge. As data centers collect more data than ever before, they need to be able to process it quickly and efficiently. According to a study by Statista, global data creation is projected to exceed 180 zettabytes by 2025. However, the current data processing landscape won’t be able to support this growth.

Increased digitization

The digitization of information and processes is another challenge. As data is increasingly generated in digital formats, it strains existing systems, making it more difficult to process in real time. This is because digital data often needs to be converted into a form that machines can process. As a result, enterprises quickly find that they need to invest in more on-premises or cloud solutions.

Real-time analytics

The need for real-time analytics is also driving the need for real-time data processing. To make decisions quickly, businesses need to be able to analyze data in near real time. This requires a different approach than batch data processing.

Data needs to be processed as it is collected rather than all at once. The problem is that the existing tools are relatively new and present a steep learning curve for users. In addition, they are often quite expensive.

See the Top DataOps Tools

Importance of Shaping Your Enterprise’s Approach to Real-Time Data Processing

While the challenges of real-time data processing can seem daunting, there are several steps that enterprises can take to shape their approach in a way that will make it more manageable.

• Start Small: Focus on one or two use cases and build from there.
• Have the Right Team in Place: Since the tools and technologies for real-time data processing are still relatively new, it is essential to have a team that is willing and able to learn new things quickly.
• Invest in Proper Infrastructure: Determine the right mix of on-premises and cloud-based solutions.
• Partner with the Right Vendors: Look for vendors with a proven track record in real-time data processing and that offer support and training.

See the Best Data Warehouse Software & Tools

How to Adapt to the Changing Data Landscape

The landscape for data processing is changing rapidly. In order to stay ahead of the curve, enterprises need to be proactive in their approach. Here are a few things that you can do:

Identify your data processing needs

Identifying your data processing needs will help you determine what type of data processing is right for your organization.

Assess your current infrastructure

Assessing your current infrastructure will help you identify any bottlenecks in your system and determine where you need to make changes.

For example, if you’re using an on-premises data processing system, you may need to migrate to a hybrid solution to allow you to scale faster. But then again, this will depend on your internal security policies and compliance needs.

Also, if you’re using an on-premises solution, it is vital to match your server components to your unique requirements. Since you can only achieve the best real-time performance at scale with the proper server hardware, you’ll need to figure out how that data is processed.

Server memory (DRAM) is costly and uses a lot of power at scale. In addition, hard drives must provide dependable long-term storage. New server persistent memory alternatives are accessible that match the speed of DRAM but are less expensive and store data in the event of a power interruption. In-memory data processing tools and databases can also be a way to speed data processing.

Invest in the right data processing tools

There are a number of data processing tools available, but not all of them are right for every organization. Therefore, it’s important to choose the right tool for your specific needs.

For example, companies in industries that rely on streaming data to operate, such as social media feeds, up-to-the-minute retail inventory management, real-time stock trades, real-time forex, ride-sharing apps, and multiplayer game interactions, require streaming data tools as well as fast, in-memory databases.

Improve data quality

Regardless of the data processing approach you choose, it is important to improve data quality. You can enhance data quality by implementing data governance policies and processes and investing in data cleansing and data enrichment tools.

See the Top Data Quality Tools & Software

Scale-up and scale-out

As data volumes continue to increase, it is crucial to have a data processing solution and architecture that can scale up and scale out. Systems are generally designed to scale up (for example, by adding more resources to an existing server or node) or scale-out (for example, by increasing the number of servers or nodes). Therefore, a database, hardware, and software solution that can scale up and scale out is ideal for real-time data processing.

A scalable data processing solution will be able to handle increased data volume without affecting performance.

Use smart data distribution

You can use data distribution techniques to reduce latency further while increasing resiliency. Some of these techniques include:

• Data sharding is the process of distributing data across multiple servers.
• Query routing is a technique that allows you to route queries to the server that contains the required data.
• Load balancing can be used to distribute data across multiple servers.

Using these techniques, you can distribute loads across multiple servers, improve performance, and eliminate hot spots in your data processing system.

Use data compression

Data compression is a method used to save storage space and improve performance by reducing the amount of data that needs to be read from disk. It is able to reduce the size of data files by removing redundant or unnecessary data.

Data compression can be done using various methods, including data deduplication, data reduction, and data archiving.

Implement data governance policies and processes

Data governance is the process of managing data. It includes defining data standards, policies, and processes. Data governance can help you improve data quality by ensuring data is accurate, consistent, and complete.

Becoming a Proactive Leader in Real-Time Data Processing

When starting your journey to real-time data processing, it’s important to keep in mind that this is an ongoing process. As the landscape continues to change, you’ll need to be proactive in your approach to data processing. This means keeping up with the latest trends and technologies and being willing to experiment with new approaches. Only by doing this will you be able to stay ahead of the curve. And when it comes to data, staying ahead of the curve is a critical competitive requirement these days.

Read next: 8 Top Data Startups

The post Tips for Processing Real-Time Data in a Data Center appeared first on IT Business Edge.

In the past few years, data has exploded in size and complexity. For example, the amount of data created, captured, copied, and consumed worldwide will hit 181 zettabytes by 2025, up from only two zettabytes in 2010.

This fact has made it difficult for businesses to promptly gather, analyze, and act on data. However, DataOps (data operations) is a software framework that was created to address this very problem.

What is DataOps?

Introduced by IBM’s Lenny Liebmann in June 2014, DataOps is a collection of best practices, techniques, processes, and solutions that applies integrated, process-oriented, and agile software engineering methods to automate, enhance quality, speed, and collaboration while encouraging a culture of continuous improvement in the field of data analytics.

DataOps began as a collection of best practices but has since grown into a novel and autonomous data analytics method. It considers the interrelatedness of the data analytics team and IT operations throughout the data lifecycle, from preparation to reporting.

Also read: 6 Ways Your Business Can Benefit from DataOps

What is the Purpose of DataOps?

DataOps aims to enable data analysts and engineers to work together more effectively to achieve better data-driven decision-making. The ultimate goal of DataOps is to make data analytics more agile, efficient, and collaborative.

To do this, there are three main pillars of DataOps:

• Automation: Automating data processes allows for faster turnaround times and fewer errors.
• Quality: Improving data quality through better governance and standardized processes leads to improved decision-making.
• Collaboration: Effective team collaboration leads to a more data-driven culture and better decision-making.

DataOps Framework

The DataOps framework is composed of four main phases:

• Data preparation involves data cleansing, data transformation, and data enrichment, which is crucial because it ensures the data is ready for analysis.
• Data ingestion handles data collection and storage. Engineers must collect data from various sources before it can be processed and analyzed.
• Data processing is the process of data transformation and data modeling to transform raw data into usable information.
• Data analysis and reporting helps businesses make better decisions by analyzing data to generate insights into trends, patterns, and relationships and reporting the results.

DataOps tools operate as command centers for DataOps. These solutions manage people, processes, and technology to provide a reliable data pipeline to customers.

In addition, these tools are primarily used by analytics and data teams across different functional areas and multiple verticals to unify all data-related development and operation processes within an enterprise.

When choosing a DataOps tool or software, businesses should consider the following features:

• Collaboration between data providers and consumers can guarantee data fluidity.
• It can act as an end-to-end solution by combining different data management practices within a single platform.
• It can automate end-to-end data workflows across the data integration lifecycle.
• Dashboard and visualization tools are available to help stakeholders analyze and collaborate on data.
• It can be deployed in any cloud environment.

Also read: How to Turn Your Business Data into Stories that Sell

5 Best DataOps Tools and Software

The following are five of the best DataOps tools and software.

Census

Census is the leading platform for operational analytics with reverse ETL (extract, transform, load), offering a single, trusted location to bring your warehouse data into your daily applications.

It sits on top of your existing warehouse and connects the data from all of your go-to-market tools, allowing everyone in your company to act on good information without requiring any custom scripts or favors from IT.

Over 50 million users receive personalized marketing thanks to Census clients’ performance improvements, including a 10x sales productivity increase due to a support time reduction of up to 98%.

In addition, many modern organizations choose Census for its security, performance, and dependability.

Key Features

• Work With Your Existing Warehouse: Because Census operates on top of your current warehouse, you can retain all your data in one location without the need to migrate to another database.
• No-Code Business Models: With the simple interface, you can build data models without writing code, allowing you to focus on your business instead of worrying about data engineering.
• Works at Scale: Census is built to handle data warehouses with billions of rows and hundreds of columns.
• Build Once, Reuse Everywhere: After you create a data model, you can use it in any tool connected to your warehouse. This means that you can build models once and use them in multiple places without having to recreate them.
• No CSV Files and Python Scripts: There is no need to export data to CSV files or write Python scripts. Census has a simple interface that allows you to build data models to integrate into sales and marketing tools without writing code.
• Fast Sync With Incremental Batch Updates: Census synchronizes data in real time, so you can always have the most up-to-date data. Incremental updates mean that you never have to wait for a complete data refresh.
• Multiple Integrations: Census integrates with all of the leading sales, marketing, collaboration, and communications tools you already use. These include Salesforce, Slack, Marketo, Google Sheets, Snowflake, MySQL, and more.

Pros

• It is easy to set up and sync a data pipeline.
• Census offers responsive and helpful support.
• The solution reduces engineering time to create a sync from your data warehouse to third-party services.

Cons

• Many integrations are still in active development and are buggy to use.

Pricing

Census has three pricing tiers:

• Free: This tier only includes 10 destination fields but is ideal for testing the tool’s features.
• Growth: At $300 per month, Growth includes 40 destination fields as well as a free trial.
• Business: At $800 per month, Business includes 100 destination fields and a free demo.
• Platform: This is a custom solution for enterprises that would like more than 100 destination fields, multiple connections, and other bespoke features.

Mozart Data

Mozart Data is a simple out-of-the-box data stack that can help you consolidate, arrange, and get your data ready for analysis without requiring any technical expertise.

With only a few clicks, SQL commands, and a couple of hours, you can make your unstructured, siloed, and cluttered data of any size and complexity analysis-ready. In addition, Mozart Data provides a web-based interface for data scientists to work with data in various formats, including CSV, JSON, and SQL.

Moreover, Mozart Data is easy to set up and use. It integrates with various data sources, including Amazon SNS, Apache Kafka, MongoDB, and Cassandra. In addition, Mozart Data provides a flexible data modeling layer that allows data scientists to work with data in various ways.

Key Features

• Over 300 Connectors: Mozart Data has over 300 data connectors that make it easy to get data from various data sources into Mozart Data without hiring a data engineer. You can also add custom connectors.
• No Coding or Arcane Syntax: With Mozart Data, there is no need to learn any coding or arcane syntax. All you need to do is point and click to get your data into the platform.
• One-Click Transform Scheduling and Snapshotting: Mozart Data allows you to schedule data transformations with a single click. You can also snapshot your data to roll back to a previous version if needed.
• Sync Your Favorite Business Intelligence (BI) Tools: Mozart Data integrates with most leading BI tools, including Tableau, Looker, and Power BI.

Pros

• The solution is easy to use and requires little technical expertise.
• It offers a wide variety of data connectors, including custom connectors.
• Users can schedule data transformations with a single click.
• Mozart Data has straightforward integrations with popular vendors such as Salesforce, Stripe, Postgres, and Amplitude.
• A Google Sheets sync is available.
• Mozart Data provides good customer support.

Cons

• Non-native integrations require some custom SQL work.
• The SQL editor is a bit clunky.

Pricing

Mozart data has three pricing tiers starting at $1,000 per month plus a $1,000 setup fee. All plans come with a free 14-day trial.

Databricks Lakehouse Platform

Databricks Lakehouse Platform is a comprehensive data management platform that unifies data warehousing and artificial intelligence (AI) use cases on a single platform via a web-based interface, command-line interface, and an SDK (software development kit).

It includes five modules: Delta Lake, Data Engineering, Machine Learning, Data Science, and SQL Analytics. Further, the Data Engineering module enables data scientists, data engineers, and business analysts to collaborate on data projects in a single workspace.

The platform also automates the process of creating and maintaining pipelines and executing ETL operations directly on a data lake, allowing data engineers to focus on quality and reliability to produce valuable insights.

Key Features

• Streamlined Data Ingestion: When new files arrive, they are handled incrementally within regular or continuous jobs. You may process new files in scheduled or ongoing processes without keeping track of state information. With no requirement for listing new files in a directory, you can track them efficiently (with the option to scale to billions of files) without listing them in a directory. Databricks infers and evolves the schema from source data as it loads into the Delta Lake.
• Automated Data Transformation and Processing: Databricks provides an end-to-end solution for data preparation, including data quality checking, cleansing, and enrichment.
• Build Reliability and Quality Into Your Data Pipelines: With Databricks, you can easily monitor your data pipelines to identify issues early on and set up alerts to notify you immediately when there is a problem. In addition, the platform allows you to version-control your pipelines, so you can roll back to a previous version if necessary.
• Efficiently Orchestrate Pipelines: With the Databricks Workflow, you can easily orchestrate and schedule data pipelines. In addition, Workflow makes it easy to chain together multiple jobs to create a data pipeline.
• Seamless Collaborations: When data has been ingested and processed, data engineers may unlock its value by allowing every employee in the company to access and collaborate on data in real time. Data engineers can use this tool to view and analyze data. In addition, they can share datasets, forecasts, models, and notebooks while also ensuring a single consistent source of truth to ensure consistency and reliability across all workloads.

Pros

• Databricks Lakehouse Platform is easy to use and set up.
• It is a unified data management platform that includes data warehousing, ETL, and machine learning.
• End-to-end data preparation with data quality checking, cleansing, and enrichment is available.
• It is built on open source and open standards, which improves flexibility.
• The platform offers good customer support.

Cons

• The pricing structure is complex.

Pricing

Databricks Lakehouse Platform costs vary depending on your compute usage, cloud service provider, and geographical location. However, if you use your own cloud, you get a 14-day free trial from Databricks, and a lightweight free trial is also available through Databricks.

Datafold

As a data observability platform, Datafold helps businesses prevent data catastrophes. It has the unique capacity to detect, evaluate, and investigate data quality concerns before they impact productivity.

Datafold offers the ability to monitor data in real time to identify issues quickly and prevent them from becoming data catastrophes. It combines machine learning with AI to provide analytics with real-time insights, allowing data scientists to make top-quality predictions from large amounts of data.

Key Features

• One-Click Regression Testing for ETL: You can go from 0–100% test coverage of your data pipelines in a few hours. With automated regression testing across billions of rows, you can also see the impact of each code change.
• Data flow Visibility Across all Pipelines and BI Reports: Datafold makes it easy to see how data flows through your entire organization. By tracking data lineage, you can quickly identify issues and fix them before they cause problems downstream.
• SQL Query Conversion: With Datafold’s query conversion feature, you can take any SQL query and turn it into a data quality alert. This way, you can proactively monitor your data for issues and prevent them from becoming problems.
• Data Discovery: Datafold’s data discovery feature helps you understand your data to draw insights from it more easily. You can explore datasets, visualize data flows, and find hidden patterns with a few clicks.
• Multiple Integrations: Datafold integrates with all major data warehouses and frameworks such as Airflow, Databricks, dbt, Google Big Query, Snowflake, Amazon Redshift, and more.

Pros

• Datafold offers simple and intuitive UI and navigation with powerful features.
• The platform allows deep exploration of how tables and data assets relate.
• The visualizations are easy to understand.
• Data quality monitoring is flexible.
• Customer support is responsive.

Cons

• The integrations they support are relatively limited.
• The basic alerts functionality could benefit from more granular controls and destinations.

Pricing

Datafold offers two product tiers, Cloud and Enterprise, with pricing dependent on your data stack and integration complexity. Those interested in Datafold will need to book a call to obtain pricing information.

dbt

dbt is a transformation workflow that allows organizations to deploy analytics code in a short time frame via software engineering best practices such as modularity, portability, CI/CD (continuous integration and continuous delivery), and documentation.

dbt Core is an open-source command-line tool allowing anyone with a working knowledge of SQL to create high-quality data pipelines.

Key Features

• Simple SQL SELECT Statements: dbt uses simple SQL SELECT statements to define data models, which makes it easy for data analysts and data engineers to get started with dbt without learning a new language.
• Pre-Packaged and Custom Testing: dbt comes with pre-packaged tests for data quality, duplication, validity, and more. Additionally, users can create their own custom tests.
• In-App Scheduling, Logging, and Alerting: dbt has an inbuilt scheduler you can use to schedule data pipelines. Additionally, dbt automatically logs all data pipeline runs and generates alerts if there are any issues.
• Version Control and CI/CD: dbt integrates with Git to easily version and deploy data pipelines using CI/CD tools such as Jenkins and CircleCI.
• Multiple Adapters: It connects to and executes SQL against your database, warehouse, platform, or query engine by using a dedicated adapter for each technology. Most adapters are open source and free to use, just like dbt.

Pros

• dbt offers simple SQL syntax.
• Pre-packaged tests and alerts are available.
• The platform integrates with Git for easy deployment.

Cons

• The command-line tool can be challenging for data analysts who are not familiar with SQL.

Pricing

dbt offers three pricing plans:

• Developer: This is a free plan available for a single seat.
• Team: $50 per developer seat per month plus 50 read-only seats. This plan includes a 14-day free trial.
• Enterprise: Custom pricing based on the required features. Prospective customers can request a free demo.

Choosing DataOps Tools

Choosing a DataOps tool depends on your needs and preferences. But, as with anything else in technology, it’s essential to do your research and take advantage of free demos and trials before settling on something.

With plenty of great DataOps tools available on the market today, you’re sure to find one that fits your team’s needs and your budget.

Read next: Top Data Quality Tools & Software 2022

The post Top DataOps Tools 2022 appeared first on IT Business Edge.

(Image source: Polaris Market Research)

Another recent study by Economist Impact and NTT that surveyed 216 C-Suite level executives found that half of the companies surveyed plan to deploy a private 5G network within the next six months to two years.

(Image source: Economist Impact 2021)

But why are the driving factors and conditions? How will companies go about building these networks? What is needed? What are the benefits and potential roadblocks?

What is Private 5G?

Private cellular networks have been around for a long time, but they are usually only used by large organizations like the military or enterprises with critical infrastructure. These private networks are designed to be isolated from the public network and offer a higher level of security and control.

Telecom operators are rolling out Public 5G for users worldwide. In contrast, Private 5G is a specialized network that businesses use to take advantage of its low latency, high availability, complete control, and enhanced personalization to promote Industry 4.0 adoption more quickly.

Private 5G Market Growth Drivers

The arrival of the Covid-19 epidemic and its subsequent recurrence in the form of second-wave and third-wave across parts of the globe compelled firms to embrace private 5G adoption because of the network’s inherent advantages. Private 5G enables low latency, high bandwidth, improved video quality, and remote sensing for virtually all verticals, effectively utilized in remote working.

As a result, businesses use private 5G to fulfill the criteria established by the post-pandemic new normal scenario, which sped up the global adoption of private 5G.

Organizations with critical communications and industrial IoT (Internet of Things) needs—such as national security organizations, the military, utilities, oil and gas businesses, mining associations, train and port operators, manufacturers, and industrial behemoths—are investing heavily in private LTE networks.

Industry 4.0 has given rise to a new generation of industrial robots that are smarter, more adaptable, and increasingly automated. Various primary industrial operations such as Siemens AG, ABB Ltd., and Mercedes-Benz AG have made significant use of sensor-based technology and industrial robotics to improve operational efficiency and productivity. The private 5G network is essential for delivering seamless and secure Internet access to Industrial IoT (IIoT) devices.

Also read: 5G and AI: Ushering in New Tech Innovation

Benefits of Private 5G

Private cellular networks offer many advantages over public networks, the most important being security, control, and customization.

• Security: A private network is designed to be isolated from the public network, which offers a higher level of security. This is because a private network can be designed with security features that are not possible on a public network. For example, a private network can be designed so that only authorized devices can connect to it.
• Control: Another advantage of a private cellular network is that it offers complete control to the network owner. The network owner can decide who can access the network and what type of traffic is allowed on the network.
• Personalization and customization: A private cellular network also offers enhanced personalization and customization options. For example, the network owner can choose to allow only certain types of devices to connect to the network or create a custom profile for each user.
• High speeds, ultra-low latency, and application support: Private cellular networks offer high speeds (1-20 Gbit/s) and low latency (1 ms), essential for applications requiring real-time data. In addition, private 5G networks can be designed to support specific applications. For example, a private network can be designed to support video conferencing or VoIP calls.
• Increased number of devices: Private 5G networks can support a high number of devices on the network. For example, due to the enhanced bandwidth, spotty Wi-Fi service in a crowded office will become a thing of the past.

Potential Roadblocks of Private 5G

The cost associated with building and maintaining a private cellular network is one of the main roadblocks companies face. To build a private 5G network, businesses must buy spectrum from the government, mobile network operators, or third-party spectrum vendors. In addition, they must obtain 5G equipment such as base stations and mini-towers from network infrastructure vendors. They also require edge devices such as routers, smartphones, embedded modules, routers, and gateways.

In addition, building out a private 5G infrastructure comes with some technical challenges. Businesses need to have expertise in-house to design and manage the network. One of the main barriers is integrating 5G with legacy systems and networks.

Another potential roadblock is that businesses may not be able to get access to the same spectrum as they would on a public network.

Proprietary technologies and the lack of standards can also be a challenge for businesses when setting up a private network. This is because there is no one-size-fits-all solution for setting up a private network. Instead, each company will need to tailor its solution based on its specific needs. 

However, even with these challenges, a private 5G network is the best option for businesses that need high security, ultra-low latency, control, customization, and need to support numerous devices.

Getting Started with Private 5G

Getting started with a private cellular network requires careful planning and execution. Organizations need to carefully assess their requirements and objectives before embarking on this journey. They also need to partner with experienced vendors who can help them navigate these challenges successfully.

Due to the challenges of rolling out and managing private 5G networks, many organizations prefer to use a managed services provider. A managed services provider (MSP) can help businesses with end-to-end planning, design, deployment, and private network management.

Companies like Cisco and Ericsson are blazing the trail in this regard. In addition, such managed private 5G services take the complexity out of building and managing a private network. This is good news for businesses that want to reap the benefits of private cellular networks without investing in the necessary resources and expertise.

Read next: Best Enterprise 5G Network Providers 2022

The post Building a Private 5G Network for Your Business  appeared first on IT Business Edge.

A patch management solution monitors and maintains software updates, ensuring that your business is protected against potential cyberattacks.

Leading Patch Management Solutions

What is Patch Management?

A software patch is a small piece of software that fixes or improves an existing program. The origin of the term “software patching” can be traced back to the early computing days in the 1940s when programmers punched computer code into a paper tape, and patches were literally pieces of tape that were stuck over the holes to correct the code.

Nowadays, patches come in digital form and are released by software vendors to fix or improve an existing program by addressing vulnerabilities, bugs, and other issues in their products. To put it simply, patch management is the process of installing these patches on your computing devices.

IT managers and security specialists use patch management tools to ensure the components of their company’s software stack and IT infrastructure are up to date. These tools track software and middleware updates and then automatically alert users or execute the updates. As a result, an employee’s responsibility to update software and remediate vulnerabilities is reduced.

To be regarded as a patch management solution, the tool must meet three critical criteria:

1. Maintain a database of software updates, middleware upgrades, and hardware upgrades.
2. Automatically notify users of new updates or automatically apply the patch.
3. Notify administrators of endpoints and users utilizing out-of-date software.

Also read: Cybersecurity Awareness for Employees: Best Practices

Why is Patch Management Important?

One of the most essential functions of patch management is to mitigate the risk of cyberattacks. According to a report by IBM and the Ponemon Institute, the average cost of a data breach for enterprises in 2021 was $4.24 million, up 10% from 2020.

(Image source: IBM.com)

The report also found that the average time it takes to detect and contain a data breach is 287 days. By keeping your software up to date, you can significantly reduce the risk of a data breach and the costly consequences that come with it.

In addition to mitigating the risk of cyberattacks, patch management also helps organizations adhere to compliance regulations. For example, the Payment Card Industry Data Security Standard (PCI DSS) requires companies to implement a patch management process to maintain the security of their systems.

Patch management is also crucial for the stability and performance of compute systems. Out-of-date software can lead to system crashes and other stability issues.

Best Patch Management Software Solutions in 2022

Here are five of the best patch management software solutions available in 2022.

Patch My PC

Patch My PC is a tool that helps enterprises using Microsoft Configuration Manager or Microsoft Intune to keep their third-party software up to date. The company has a great track record of success. In 2021, 3,792 third-party updates were released by Patch My PC, including 1,128 security fixes and 1,412 Common Vulnerabilities and Exposures (CVEs).

Features

• Create SCCM and Intune Applications: Beyond patching, you can create applications for the initial deployment of solutions in Microsoft SCCM and Intune. It includes icons, keywords, a description, and much more.
• Auto-Update Applications: Patch My PC will automatically update applications when a new patch is released. Updates are downloaded, extracted, and installed, all without user interaction.
• Deploy Using Task Sequences or Collections: Easily deploy applications using Task Sequences in SCCM or Collections in Intune.
• Run Custom Scripts: Sometimes, you need to do more than patch an application. With Patch My PC, you can run custom scripts before and after installations.
• Disable Self-Updates for Applications: Disable self-updates for applications you do not want to be automatically updated.
• Enable Standard Logging for Installations: Get detailed information about every installation with standard logging enabled.

Pros

• Low-cost solution per device
• Easy to configure and navigate
• Timesaver when it comes to patching common applications
• Excellent support

Cons

• Creating “customized” packages such as Cisco AnyConnect is complicated for new users.

Pricing

Patch My PC has three pricing tiers, as shown below.

Each plan comes with a 30-day free trial. You can also book a product demo before signing up for the free trial.

Symantec Endpoint Management

Symantec Endpoint Management, acquired by Broadcom in 2019, is a patch management solution that helps organizations of all sizes secure and manage their endpoints. Broadcom has over 50 years of experience in the technology space and has millions of customers worldwide.

Features

• Centralized Patch Management: Symantec Endpoint Management provides a single view for managing patches and updates for all endpoints in your organization.
• Automated Patching: The tool can automate the patching process for both Microsoft and third-party applications.
• Intuitive Dashboard: The Symantec Endpoint Management dashboard provides a snapshot of the health of your endpoints and allows you to take action quickly if needed.
• Security Intelligence: The tool includes security intelligence features that allow you to see the latest threats and how they are impacting your organization.
• Integrated Endpoint Protection: Symantec Endpoint Management includes integrated endpoint protection features to help you secure your endpoints.
• Real-time and historical data: Real-time actionable compliance reports allow you to make quick, informed decisions to keep your environment secure, while automation reduces costs even more.

Pros

• Broadcom’s experience and expertise
• In-depth security intelligence
• Real-time reporting

Cons

• It can be slow when multiple users are on the console.

Pricing

The company does not publish pricing information on its website but provides potential customers with a dedicated page to find a partner or distributor.

ManageEngine Patch Manager Plus

ManageEngine Patch Manager scans endpoints to discover missing patches, validates patches before deployment to eliminate security risks, and automates patch rollout to operating systems and third-party applications for improved visibility and control.

The company is the IT management division of Zoho Corporation and has over 120 award-winning IT products and tools.

Features

• Automated Patch Management: The patch management process is automated for Microsoft and third-party applications.
• Cross-Platform Support: ManageEngine Patch Manager supports Windows, Mac, Linux, and VMware operating systems.
• Test & Approve Patches: Patches are validated before deployment to eliminate security risks.
• Ensure Patch Compliance: ManageEngine Patch Manager tracks patch compliance and generates insightful reports.
• Remote Patch Management: ManageEngine Patch Manager enables you to manage patches for devices not on the local network.

Pros

• Intuitive and straightforward dashboard design
• Good customer support
• Excellent cross-platform support
• Relatively inexpensive compared to competitors

Cons

• It does not allow you to select software updates by the user, only by machine.

Pricing

ManageEngine Patch Manager Plus has several pricing points depending on the number of devices and whether you want an on-premises or cloud solution.

SolarWinds Patch Manager

SolarWinds Patch Manager makes deploying updates on tens of thousands of servers and workstations quick and straightforward. It also allows you to utilize and expand on Microsoft WSUS or SCCM’s reporting, deployment, and management capabilities for both third-party and Microsoft patches.

Serving 498 of the Fortune 500, the Austin, Texas-based company has been in business for over 20 years.

Features

• Microsoft WSUS Patch Management: It helps simplify the whole WSUS patch management process, from patch notification and synchronization to approvals and deployment.
• Integrations with SCCM: It integrates with Microsoft’s System Center Configuration Manager (SCCM) for comprehensive patch management across heterogeneous environments.
• Third-Party Application Patching: SolarWinds Patch Manager enables you to patch third-party applications and the regular Microsoft updates.
• Prebuilt/Pretested Packages: It reduces deployment time and risk by providing prebuilt, pretested software update packages.
• Patch Compliance Reports: SolarWinds Patch Manager tracks patch compliance and generates insightful reports.
• Patch Status Dashboard: The platform provides at-a-glance information on the health of your patching operations.

Pros

• Comprehensive feature set
• Good customer support
• Easy to demonstrate compliance with out-of-the-box reports and dashboard views
• Good integration with other SolarWinds products
• Vibrant 150,000+ user community

Cons

• It’s difficult to push out third-party updates that are not officially approved by SolarWinds.

Pricing

SolarWinds Patch Manager has two licensing options, subscription or perpetual, which is based on the number of nodes, or endpoints, managed. Subscription pricing starts at $2,006, while perpetual licensing starts at $3,997. The company also offers a fully functional 30-day free trial.

PDQ Deploy

Established in 2001, PDQ Deploy is a Salt Lake City, Utah-based patch management software designed to help you automate the process. You can update third-party programs and script deployment and make significant system modifications in just a few minutes.

Users may select the software they wish to install and, if necessary, update specific machines and establish their desired schedule for deployment. PDQ will automatically and quietly apply updates once the deployment has been scheduled without disrupting end users.

PDQ Deploy works best in combination with PDQ Inventory, which scans, identifies, and removes undesired or out-of-date applications deployed by your end users.

Features

• Schedule Remote Multi-Step, Multi-Application Custom Deployments: You can update programs on your computers on your preferred schedule, even if you’re away from the office.
• Execute Commands, Run Scripts, and Force Reboots: With just a few clicks, you can deploy software, reboot machines, and run scripts. This is a powerful tool for admins who need to make changes or repairs on many devices quickly.
• 250+ Ready-to-Deploy Common Applications: PDQ Deploy has a library of over 250+ common applications ready-to-deploy. This eliminates the need for you to hunt for software updates and makes the deployment process quick and easy.
• Automatic Retry: If a deployment fails for any reason, PDQ Deploy will automatically try again.
• Email Status Updates: Get email updates on the progress of your deployments, allowing you to stay informed on the go.
• Deploy Using Active Directory, Spiceworks, and PDQ Inventory: You can deploy software to your machines using Active Directory, Spiceworks, or PDQ Inventory. This gives you flexibility and choice when it comes to deployment.

Pros

• Ease of use
• Frequent updates
• Comprehensive PDQ library
• Good customer support that includes community forums

Cons

• Sometimes, very large packages fail.

Pricing

PDQ Deploy offers three pricing tiers as follows:

1. We ❤ Underdogs: $1,275 per admin per year
2. Deploy + Inventory: $1,500 per admin per year
3. Enterprise: For more than 15 licenses, customers can get a custom pricing plan

All plans come with a 14-day free trial.

How to Choose Patch Management Software

Ultimately, the decision of which patch management software to choose depends on your organization’s specific needs. However, based on our analysis of the top five tools, a few key factors emerge.

• Ease of Use: The patch management software should be easy to use, even for those who are not tech-savvy.
• Frequent Updates: The software should be frequently updated to ensure that you have the latest security patches and features.
• Comprehensive Library: The software should have a comprehensive library of software updates, so you do not have to hunt for them yourself.
• Good Customer Support: The software should come with good customer support, including community forums where you can get help from other users.
• Price: The software should be affordable for your organization but not the sole determinant. It should also come with a free trial to allow you to try it before you buy.

We hope that this article has helped you understand the basics of patch management and given you a few points to consider as you make your decision. At the very least, we hope you better understand the importance of patch management and why it should be a critical part of your security strategy.

Read next: Top Digital and Computer Forensics Tools & Software 2022

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What is Network Access Control?

The best way to understand network access control is to think about an office block and its security. An office building typically has doors, floor levels, lifts, and various offices at each level. Access to each level or company office is restricted to company employees, while guests usually have designated areas. There are also access restrictions for specific staff within each organization’s office. Enforcement is done using various methods such as biometric access controls, smart cards, password-locked doors, or physical methods such as security guards.

Network access control works similarly. Substitute the office building for a corporate network. Network access restrictions are enforced by limiting access to certain areas of the network based on user identity, device security, and other network policies.

NAC software is a network security technology that limits access to a private network until the user or device has been authenticated and meets predefined security policies.

Also read: Understanding the Zero Trust Approach to Network Security

How to Choose a NAC Solution?

When looking for a NAC solution, there are several features you need to consider. The most important are:

• Ecosystem Compatibility and Integration: You must ensure that the NAC solution you choose is compatible with the other security solutions you have in place. The NAC solution must integrate well into your existing environment to avoid conflicts or disruptions.
• Agent-based or Agentless: Another critical consideration is whether you want an agent-based or agentless solution. Agent-based solutions require installing a small piece of software on each device that needs to be monitored. Agentless solutions don’t require any software to be installed and can be more efficient in large environments. However, agentless solutions can be more difficult to troubleshoot if something goes wrong.
• Ease of Use for Administrators: The NAC solution should be easy to use for administrators. The solution must be intuitive and have a user-friendly GUI. If the solution is difficult to navigate, administrators may not use it correctly or at all.
• Device Limits: You also need to decide how many devices or endpoints you want the NAC solution to monitor. Some solutions can only monitor a certain number of devices, while others have no limit. This will also have a pricing implication.
• Temporary Guest Access: Guest access is becoming an increasingly important feature for companies. Employees often need to bring their devices into the office or give guests temporary access to company resources. The best NAC solutions will have a way to easily and securely give guests temporary access to the network.
• Regulatory Compliance: Depending on your industry, you may need to comply with certain regulatory requirements. Make sure the NAC solution you choose is compliant with any relevant regulations.
• How Well the Solution Scales as Your Company Grows: As a company grows, its IT needs will also grow. Make sure the NAC solution you choose can scale along with your company. Otherwise, you’ll need to replace it as your company grows, which can be costly and disruptive.
• Value-added Services: Some NAC solutions come with value-added services such as vulnerability management or intrusion detection. These can be helpful, your overall cost of acquisition for IT security services.

Also read: Top Infrastructure Monitoring Tools 2022

5 Best Network Access Control (NAC) Solutions

We reviewed the various network access control solutions on the market. Below are the top five vendors in this field based on our analysis and evaluation.

Twingate

Twingate is a remote access solution for private applications, data, and environments on-premise or in the cloud. It replaces outdated business VPNs that were not designed to handle a world where “work from anywhere” and cloud-based assets are increasingly common.

Twingate’s cutting-edge zero-trust network security strategy boosts security while retaining simplicity.

Key Features

• Zero-trust network: Twingate’s zero-trust network security strategy is based on the principle that a network should not trust users and devices until authenticated. The network is segmented into different security zones, and each user is only given access to the resources they need.
• Software-only solution: Twingate is a software-only solution, which means no hardware is required. This makes it easy to deploy and can be used with existing infrastructure without requiring changes.
• Least privilege access at the application level: Users are only given the minimum amount of access they need to perform their job. This reduces the risk of data breaches and unauthorized access.
• Centralized admin console: The Twingate admin console is web-based and is accessible anywhere. It manages users, applications, and devices.
• Effortless scaling: Twingate can be easily scaled as your company grows. There is no need to add hardware, segment networks, or make changes to your existing infrastructure.
• Easy client agent setup: The Twingate client agent can be installed by users without IT support. This makes it easy to deploy and reduces the burden on IT staff.
• Split tunneling: Split tunneling allows users to access local and remote resources simultaneously. This reduces network congestion and improves performance.

Pros

• Uses a zero-trust approach to network access.
• Intuitive and easy to use.
• Simple documentation.
• Quick setup.

Cons

• Lacks a GUI client for Linux.

Pricing

Twingate has three pricing tiers as follows:

F5 BIG-IP Access Policy Manager

F5 BIG-IP Access Policy Manager manages global access to users’ networks, cloud providers, applications, and API endpoints. F5 BIG-IP APM unifies authentication for remote clients and devices, distributed networks, virtual environments, and web access.

F5 BIG-IP supports modern and legacy authentication and authorization protocols and procedures. When applications cannot use modern authentication and authorization standards such as SAML or OAuth with OIDC, BIG-IP APM converts user credentials into the proper authentication standard required by the application.

Key Features

• Identity-aware proxy (IAP): The identity-aware proxy (IAP) is a key feature of F5 BIG-IP that deploys the Zero Trust model. It inspects all traffic to and from the protected application, regardless of location. This provides granular visibility and control of user activity.
• Identity federation, MFA, and SSO: Identity federation allows companies to manage access to multiple applications with a single identity provider. F5 BIG-IP supports multi-factor authentication (MFA) and single sign-on (SSO). This feature provides an additional layer of security for remote and mobile users.
• Secure remote and mobile access: F5 BIG-IP provides secure remote and mobile access to company applications and data. SSL VPN in conjunction with a secure and adaptive per-app VPN unifies remote access identities.
• Secure and managed web access: The tool provides a secure web gateway to protect against malicious activity. It uses a web app proxy to centralize authentication, authorization, and endpoint inspection.
• API protection: F5 BIG-IP provides secure authentication for REST APIs, integrating OpenAPI files. 
• Offload and simplify authentication: For a smooth and secure user experience across all apps, it uses SAML, OAuth, and OIDC.
• Dynamic split tunneling: F5 BIG-IP offers dynamic split tunneling, allowing users to access both local and remote resources simultaneously. This reduces network congestion and improves performance.
• Central management and deployment: The tool provides a central management console for easy deployment of policies across all applications.
• Performance and scalability: F5 BIG-IP supports up to 1 million access sessions on a single BIG-IP device and up to 2 million on a VIPRION chassis.

Pros

• Centralized management.
• Easy to troubleshoot.
• Secure remote and mobile access.
• API protection.
• Dynamic split tunneling.

Cons

• Logs can be complicated to read.

Pricing

The company does not publish pricing information but provides a free demo and free trial. Contact the company for custom pricing in all business models including subscription, Enterprise License Agreements (ELAs), perpetual licenses, and public cloud marketplace.

Cisco ISE (Identity Services Engine)

Cisco is an internationally acclaimed cybersecurity leader. Its ISE is a specialized network access control product that increases security and reduces the risk of data breaches.

Cisco ISE uses the 802.11X standard to authenticate and authorize devices on a network. It also uses posture assessment to ensure that each endpoint meets certain security criteria before being granted access.

Cisco ISE supports a wide range of devices, including Windows, Mac, Linux, and Android. It also supports various authentication methods, including Active Directory, LDAP, RADIUS, TACACS+, and XTACACS+.

Key Features

• Software-defined network segmentation: This feature extends zero trust and reduces the attack surface. In addition, it limits the spread of ransomware in the event of a breach and allows admins to rapidly contain the threat.
• Policy creation and management: Cisco ISE allows administrators to create granular access policies based on user identity or device posture. Admins can apply these policies to any network resource, including wired, wireless, and VPN networks.
• Guest access: The tool provides a secure guest portal that allows guests to access the internet without compromising the security of the corporate network. In addition, admins can customize the guest portal to match the company’s branding.
• Reporting and analytics: Cisco ISE provides comprehensive reports on all activity across the network. These reports can be used to identify security threats, assess compliance, and troubleshoot network issues.
• Device profiling: It uses device profiling to create a database of authorized devices. This feature allows administrators to quickly and easily grant or deny access to specific devices.
• Integration: Cisco ISE integrates with a wide range of other Cisco products, including the Catalyst series switches, the ASA firewalls, and the Cloud Services Router.

Pros

• Wide range of authentication methods.
• Comprehensive reporting and analytics.
• Device profiling.
• Integration with other Cisco products.

Cons

• The UI presents a steep learning curve.

Pricing

Cisco does not publish pricing information. Most customers contact Cisco partners to purchase Cisco ISE.

FortiNAC

The FortiNAC product line consists of hardware and virtual machines. A Control and an Application server are required for each FortiNAC deployment. If your installation needs more capacity than a single server can provide, you may stack servers to gain additional capacity. There is no maximum number of concurrent ports.

It can be deployed on-premises, in the cloud, or as a hybrid solution.

Key Features

• Agentless scanning: FortiNAC uses agentless scanning to detect and assess devices. This feature eliminates the need to install software on every device and allows you to scan devices not connected to the network.
• 17 profiling methods: FortiNAC uses 17 methods to profile devices and determine their identity.
• Simplified onboarding: FortiNAC provides a simplified, automated onboarding process for a large number of users, endpoints, and guests.
• Micro-segmentation: FortiNAC allows you to create micro-segments that segment devices into specific zones. This feature reduces the risk of a breach spreading throughout the network.
• Extensive multi-vendor support: You can manage and interact with network devices (switches, wireless access points, firewalls, clients) from over 150 vendors using FortiNAC.
• Scalability: The FortiNAC architecture is ideal for scale across multiple locations.

Pros

• Easy to implement and manage.
• Good customer support.
• Complete device visibility.
• Simple onboarding.
• Extensive multi-vendor support.

Cons

• Limited third-party native integration.

Pricing

Customers can get pricing information by requesting a quote. You can also sign up for a free demo or start a free trial.

Aruba ClearPass Access Control and Policy Management

Aruba is a Hewlett Packard (HP) company. Clearpass uses policies and granular security controls—such as how and where the connected traffic can navigate throughout the network— to ensure that authorized access is given to users in both wired and wireless business networks.

Key Features

• Agentless policy control and automated response: ClearPass uses agentless policy control and automated response to detect and assess devices. The Aruba ClearPass Policy Manager allows you to put in place real-time policies for users and devices connecting and what they can access.
• AI-based insights, automated workflows, and continuous monitoring: ClearPass has built-in artificial intelligence (AI) that provides insights, automated workflows, and continuous monitoring. This helps you to quickly identify issues and automate the response.
• Dynamically enforced access privileges: ClearPass gives you the ability to dynamically enforce access privileges for authorized users, devices, and applications. You can also create custom policies that fit your specific needs.
• Secured access for guests, corporate devices, and BYOD: Aruba ClearPass provides secure access for guests, corporate devices, and Bring Your Own Device (BYOD). It uses role-based access control to give you granular control over what users can do on the network.
• Scale and resilience: The ClearPass platform is designed to scale and be resilient. It can handle large volumes of traffic and has a high availability architecture.

Pros

• Uses AI-based insights.
• Highly scalable and excellent for large enterprises.
• Integrates with more than 170 IT management solutions.
• Supports multiple authentication protocols. 

Cons

• Some customers have found support to be hit-or-miss.

Pricing

Aruba does not publish pricing information. Pricing models include subscription and perpetual licenses. You can also try out a fully interactive demo.

Getting Started with a NAC Solution

Choosing the right Network Access Control (NAC) solution can be overwhelming. There are many different options on the market, and each one has its own set of unique features. The best way to find the right NAC solution for your business is to consider your specific needs and compare solutions that fit those needs.

Read next: Evolving Digital Transformation Implementation with Hybrid Architectures

The post Best Network Access Control 2022: NAC Solutions appeared first on IT Business Edge.

Network security and management is a complex and ever-evolving field. To stay ahead of the latest threats, you need to know the latest trends and acronyms. Here are some of the most important network security trends and acronyms.

What is Network Security?

Network security can be defined as the practice of protecting networked systems, including hardware, software, and data, from unauthorized access or theft. It includes configurations and rules to protect against attacks and physical security measures to deter and detect intruders.

Network security is essential because it helps protect sensitive information from unauthorized individuals. It also helps prevent denial-of-service (DoS) attacks, rendering a system unusable.

In today’s connected world, where technologies like 5G and the Internet of Things (IoT) are becoming more prevalent, network security is more important than ever. Network administrators have to deal with a constantly evolving and sophisticated threat environment where cyber criminals are always looking for vulnerabilities to exploit.

According to a report by Barracuda dubbed The state of network security in 2021, 81% of those surveyed said their company had suffered at least one security breach in the last year, while 74% of respondents said their company had suffered at least one ransomware attack in the past year.

And network attacks are no longer perpetrated by lone individuals or teams of a few people today. Instead, attacks are now conducted by governments, by companies against competitors, and by large transnational criminal networks.

Network administrators need to be aware of the latest threats and take appropriate measures to protect their systems.

Network Security Trends

Several network security trends are making headlines. Here are some of the most recent developments in network security management.

Zero trust security model

The zero trust security model is a security concept that advocates for a “zero trust” approach to security where organizations do not automatically trust any individual or entity, device, or application on the network. It is essentially an “assume breach” mentality in acknowledgment of the breakdown of the traditional security perimeter.

The zero trust model was first coined by Forrester Research in 2010 and has gained popularity in recent years as more organizations move to cloud-based and hybrid environments. Some of the world’s largest tech companies, such as Google, IBM, Oracle, Microsoft, and even the U.S. government, have adopted the zero trust security model.

Under the zero trust model, all users, devices, and applications on the network are untrusted until they can be authenticated and authorized. Network administrators carefully assess all risks before granting access to users, devices, or applications.

Cybersecurity education and knowledge sharing

According to a joint study by Stanford University and security company Tessian, almost 90% of all data breaches are caused by human error. So, it would be reasonable to assume that one of the best ways to combat data breaches is to educate employees on cybersecurity best practices.

Organizations are starting to realize the importance of training their employees on cybersecurity. As a result, many organizations are now making cybersecurity education a requirement for all employees. The goal is to educate employees on how to identify threats and prevent attacks and change the organization’s culture, so cybersecurity is top of mind for everyone.

A focus on incident detection and response (IDR)

Organizations are now placing a greater emphasis on incident detection and response (IDR). IDR is all about detecting security incidents as they happen and then responding in a way that minimizes the damage.

IDR requires a proactive approach to security where organizations are constantly on the lookout for suspicious activity. Once an incident is detected, it is important to have a plan to carry out the appropriate response quickly and effectively.

AI for network security

Artificial intelligence (AI) is being increasingly used in network security since it can be used for tasks such as identifying malicious traffic, malware detection, and data analysis. AI also offers many benefits for network security. For example, AI systems are trained to generate threat alerts, identify new malware types, and protect sensitive data.

Several companies offer AI-based network security tools, including some of the most well-known providers like Cisco, CrowdStrike, and Fortinet.

Also read: The Pros and Cons of Enlisting AI for Cybersecurity

Combining NetOps with SecOps

NetOps and SecOps are two disciplines that are often siloed in most organizations. However, there is a growing trend of organizations combining the two disciplines into a single team.

NetOps is the practice of managing and operating a network. SecOps is the practice of securing a network. By combining the two disciplines into a single team, organizations can create a more holistic approach to network security.

A move to hybrid environments

More and more organizations are moving to hybrid environments, which involves a mix of on-premises and cloud-based infrastructure. The benefits of a hybrid environment include increased flexibility, scalability, and cost savings. However, it is important to note that hybrid environments have their own set of security challenges.

For example, data in a hybrid environment is often spread across multiple platforms, making it more difficult to secure. In addition, hybrid environments are often more complicated, leading to increased complexity in the network security stack.

The trend, therefore, is a more consolidated, security-oriented view of an application’s performance. This approach necessitates tools that offer visibility across different environments. It also requires combining cloud-based and traditional network-based monitoring methods.

Also read: Disaster Recovery Across Hybrid Cloud Infrastructures

4 Popular Network Security Acronyms

Below are four of the most popular network security acronyms.

BYOD: Bring your own device

BYOD is the workplace practice of allowing employees to bring their own devices (such as laptops, smartphones, and tablets) to work and use them for business purposes.

BYOD can be a security risk because it increases the number of connected devices to the network and increases the threat surface. In addition, many employees are not aware of the risks associated with using personal devices for work purposes.

To address this risk, organizations often come up with BYOD policies that stipulate the rules and guidelines for using personal devices at work. Employees that want to join an organization’s BYOD program must agree to the terms of the policy.

ZTN: Zero trust network

A zero trust network operates in accordance with the zero trust security model outlined above. This means that every device and user must be authenticated and authorized before being granted access to any resources.

SASE: Secure access service edge

The secure access service edge (SASE) is an enterprise networking category introduced by Gartner in 2019. SASE provides organizations with a way to securely connect users to applications and data regardless of location.

In the past, administrators implemented network access with siloed point solutions, which was complex and expensive. This approach hampered IT agility.

SASE allows enterprises to shorten new product development and delivery and respond quickly to changes in the business environment.

XDR: Extended detection and response

Extended detection and response (XDR) is a security solution that provides visibility into all aspects of an organization’s IT environment. XDR solutions are designed to detect, investigate, and respond to threats across the entire attack surface.

With new threats and vulnerabilities popping up every day, it can be hard to keep track of all the latest trends and acronyms. Enterprise network administrators must remain vigilant and regularly apprised of the latest trends and developments to maintain a secure network.

Read next: Best Vulnerability Management Tools 2022

The post Network Security Trends and Acronyms that You Must Know appeared first on IT Business Edge.

This is where MLOps comes in. MLOps is the practice of optimizing the continuous delivery of ML models, and it brings a host of benefits to organizations.

Below we explore the definition of MLOps, its benefits, and how it compares to AIOps. We also look at some of the top MLOps tools and platforms.

What Is MLOps?

MLOps combines machine learning and DevOps to automate, track, pipeline, monitor, and package machine learning models. It began as a set of best practices but slowly morphed into an independent ML lifecycle management approach. As a result, it applies to the entire lifecycle, from integrating data and model building to the deployment of models in a production environment.

MLOps is a special type of ModelOps, according to Gartner. However, MLOps is concerned with operationalizing machine learning models, whereas ModelOps focuses on all sorts of AI models.

Benefits of MLOps

The main benefits of MLOps are:

• Faster time to market: By automating deploying and monitoring models, MLOps enables organizations to release new models more quickly.
• Improved accuracy and efficiency: MLOps helps improve models’ accuracy by tracking and managing the entire model lifecycle. It also enables organizations to identify and fix errors more quickly.
• Greater scalability: MLOps makes it easier to scale up or down the number of machines used for training and inference.
• Enhanced collaboration: MLOps enables different teams (data scientists, engineers, and DevOps) to work together more effectively.

MLOps vs. AIOps: What are the Differences?

AIOps is a newer term coined in response to the growing complexity of IT operations. It refers to the application of artificial intelligence (AI) to IT operations, and it offers several benefits over traditional monitoring tools.

So, what are the key differences between MLOps and AIOps?

• Scope: MLOps is focused specifically on machine learning, whereas AIOps is broader and covers all aspects of IT operations.
• Automation: MLOps is largely automated, whereas AIOps relies on human intervention to make decisions.
• Data processing: MLOps uses pre-processed data for training models, whereas AIOps processes data in real time.
• Decision-making: MLOps relies on historical data to make decisions, whereas AIOps can use real-time data.
• Human intervention: MLOps requires less human intervention than AIOps.

Types of MLOps Tools

MLOps tools are divided into four major categories dealing with:

1. Data management
2. Modeling
3. Operationalization
4. End-to-end MLOps platforms

Data management

• Data Labeling: Large quantities of data, such as text, images, or sound recordings, are labeled using data labeling tools (also known as data annotation, tagging, or classification software). Labeled information is fed into supervised ML algorithms to generate new, unclassified data predictions.
• Data Versioning: Data versioning ensures that different versions of data are managed and tracked effectively. This is important for training and testing models as well as for deploying models into production.

Modeling

• Feature Engineering: Feature engineering is the process of transforming raw data into a form that is more suitable for machine learning algorithms. This can involve, for example, extracting features from data, creating dummy variables, or transforming categorical data into numerical features.
• Experiment Tracking: Experiment tracking enables you to keep track of all the steps involved in a machine learning experiment, from data preparation to model selection to final deployment. This helps to ensure that experiments are reproducible and the same results are obtained every time.
• Hyperparameter Optimization: Hyperparameter optimization is the process of finding the best combination of hyperparameters for an ML algorithm. This is done by running multiple experiments with different combinations of hyperparameters and measuring the performance of each model.

Operationalization

• Model Deployment/Serving: Model deployment puts an ML model into production. This involves packaging the model and its dependencies into a format that can be run on a production system.
• Model Monitoring: Model monitoring is tracking the performance of an ML model in production. This includes measuring accuracy, latency, and throughput and identifying any problems.

End-to-end MLOps platforms

Some tools go through the machine learning lifecycle from end to end. These tools are known as end-to-end MLOps platforms. They provide a single platform for data management, modeling, and operationalization. In addition, they automate the entire machine learning process, from data preparation to model selection to final deployment.

Also read: Top Observability Tools & Platforms

Best MLOps Tools & Platforms

Below are five of the best MLOps tools and platforms.

SuperAnnotate: Best for data labeling & versioning

Superannotate is used for creating high-quality training data for computer vision and natural language processing. The tool enables ML teams to generate highly precise datasets and effective ML pipelines three to five times faster with sophisticated tooling, QA (quality assurance), ML, automation, data curation, strong SDK (software development kit), offline access, and integrated annotation services.

In essence, it provides ML teams with a unified annotation environment that offers integrated software and service experiences that result in higher-quality data and faster data pipelines.

Key Features

• Pixel-accurate annotations: A smart segmentation tool allows you to separate images into numerous segments in a matter of seconds and create clear-cut annotations.
• Semantic and instance segmentation: Superannotate offers an efficient way to annotate Label, Class, and Instance data.
• Annotation templates: Annotation templates save time and improve annotation consistency.
• Vector Editor: The Vector Editor is an advanced tool that enables you to easily create, edit, and manage image and video annotations.
• Team communication: You can communicate with team members directly in the annotation interface to speed up the annotation process.

Pros

• Easy to learn and user-friendly
• Well-organized workflow
• Fast compared to its peers
• Enterprise-ready platform with advanced security and privacy features
• Discounts as your data volume grows

Cons

• Some advanced features such as advanced hyperparameter tuning and data augmentation are still in development.

Pricing

Superannotate has two pricing tiers, Pro and Enterprise. However, actual pricing is only available by contacting the sales team.

Iguazio: Best for feature engineering

Iguazio helps you build, deploy, and manage applications at scale.

New feature creation based on batch processing necessitates a tremendous amount of effort for ML teams. These features must be utilized during both the training and inference phases.

Real-time applications are more difficult to build than batch ones. This is because real-time pipelines must execute complex algorithms in real-time.

With the growing demand for real-time applications such as recommendation engines, predictive maintenance, and fraud detection, ML teams are under a lot of pressure to develop operational solutions to the problems of real-time feature engineering in a simple and reproducible manner.

Iguazio overcomes these issues by providing a single logic for generating real-time and offline features for training and serving. In addition, the tool comes with a rapid event processing mechanism to calculate features in real time.

Key Features

• Simple API to create complex features: Allows your data science staff to construct sophisticated features with a basic API (application programming interface) and minimize effort duplication and engineering resources waste. You can easily produce sliding windows aggregations, enrich streaming events, solve complex equations, and work on live-streaming events with an abstract API.
• Feature Store: Iguazio’s Feature Store provides a fast and reliable way to use any feature immediately. All features are stored and managed in the Iguazio integrated feature store.
• Ready for production: Remove the need to translate code and break down the silos between data engineers and scientists by automatically converting Python features into scalable, low-latency production-ready functions.
• Real-time graph: To easily make sense of multi-step dependencies, the tool comes with a real-time graph with built-in libraries for common operations with only a few lines of code.

Pros

• Real-time feature engineering for machine learning
• It eliminates the need for data scientists to learn how to code for production deployment
• Simplifies the data science process
• Highly scalable and flexible

Cons

• Iguazio has poor documentation compared to its peers.

Pricing

Iguazio offers a 14-day free trial but doesn’t publish any other pricing information on its website.

Neptune.AI: Best for experiment tracking

Neptune.AI is a tool that enables you to keep track of all your experiments and their results in one place. You can use it to monitor the performance of your models and get alerted when something goes wrong. With Neptune, you can log, store, query, display, categorize, and compare all of your model metadata in one place.

Key Features

• Full model building and experimentation control: Neptune.AI offers a single platform to manage all the stages of your machine learning models, from data exploration to final deployment. You can use it to keep track of all the different versions of your models and how they perform over time.
• Single dashboard for better ML engineering and research: You can use Neptune.AI’s dashboard to get an overview of all your experiments and their results. This will help you quickly identify which models are working and which ones need more adjustments. You can also use the dashboard to compare different versions of your models. Results, dashboards, and logs can all be shared with a single link.
• Metadata bookkeeping: Neptune.AI tracks all the important metadata associated with your models, such as the data they were trained on, the parameters used, and the results they produced. This information is stored in a searchable database, making it easy to find and reuse later. This frees up your time to focus on machine learning.
• Efficient use of computing resources: Neptune.AI allows you to identify under-performing models and save computing resources quickly. You can also reproduce results, making your models more compliant and easier to debug. In addition, you can see what each team is working on and avoid duplicating expensive training runs.
• Reproducible, compliant, and traceable models: Neptune.AI produces machine-readable logs that make it easy to track the lineage of your models. This helps you know who trained a model, on what data, and with what settings. This information is essential for regulatory compliance.
• Integrations: Neptune.AI integrates with over 25 different tools, making it easy to get started. You can use the integrations to pipe your data directly into Neptune.AI or to output your results in a variety of formats. In addition, you can use it with popular data science frameworks such as TensorFlow, PyTorch, and scikit-learn.

Pros

• Keeps track of all the important details about your experiments
• Tracks numerous experiments on a single platform
• Helps you to identify under-performing models quickly
• Saves computing resources
• Integrates with numerous data science tools
• Fast and reliable

Cons

• The user interface needs some improvement.

Pricing

Neptune.AI offers four pricing tiers as follows:

• Individual: Free for one member and includes a free quota of 200 monitoring hours per month and 100GB of metadata storage. Usage above the free quota is charged.
• Team: Costs $49 per month with a 14-day free trial. This plan allows unlimited members and has a free quota of 200 monitoring hours per month and 100GB of metadata storage. Usage above the free quota is charged. This plan also comes with email and chat support.
• Scale: With this tier, you have the option of SaaS (software as a service) or hosting on your infrastructure (annual billing). Pricing starts at $499 per month and includes unlimited members, custom metadata storage, custom monitoring hours quota, service accounts for CI workflows, single sign-on (SSO), onboarding support, and a service-level agreement (SLA).
• Enterprise: This plan is hosted on your infrastructure. Pricing starts at $1,499 per month (billed annually) and includes unlimited members, Lightweight Directory Access Protocol (LDAP) or SSO, an SLA, installation support, and team onboarding.

Kubeflow: Best for model deployment/serving

Kubeflow is an open-source platform for deploying and serving ML models. Google created it as the machine learning toolkit for Kubernetes, and it is currently maintained by the Kubeflow community.

Key Features

• Easy model deployment: Kubeflow makes it easy to deploy your models in various formats, including Jupyter notebooks, Docker images, and TensorFlow models. You can deploy them on your local machine, in a cloud provider, or on a Kubernetes cluster.
• Seamless integration with Kubernetes: Kubeflow integrates with Kubernetes to provide an end-to-end ML solution. You can use Kubernetes to manage your resources, deploy your models, and track your training jobs.
• Flexible architecture: Kubeflow is designed to be flexible and scalable. You can use it with various programming languages, data processing frameworks, and cloud providers such as AWS, Azure, Google Cloud, Canonical, IBM cloud, and many more.

Pros

• Easy to install and use
• Supports a variety of programming languages
• Integrates well with Kubernetes at the back end
• Flexible and scalable architecture
• Follows the best practices of MLOps and containerization
• Easy to automate a workflow once it is properly defined
• Good Python SDK to design pipeline
• Displays all logs

Cons

• An initial steep learning curve
• Poor documentation

Pricing

Open-source

Databricks Lakehouse: Best end-to-end MLOPs platform

Databricks is a company that offers a platform for data analytics, machine learning, and artificial intelligence. It was founded in 2013 by the creators of Apache Spark. And over 5,000 businesses in more than 100 countries—including Nationwide, Comcast, Condé Nast, H&M, and more than 40% of the Fortune 500—use Databricks for data engineering, machine learning, and analytics.

Databricks Machine Learning, built on an open lake house design, empowers ML teams to prepare and process data while speeding up cross-team collaboration and standardizing the full ML lifecycle from exploration to production.

Key Features

• Collaborative notebooks: Databricks notebooks allow data scientists to share code, results, and insights in a single place. They can be used for data exploration, pre-processing, feature engineering, model building, validation and tuning, and deployment.
• Machine learning runtime: The Databricks runtime is a managed environment for running ML jobs. It provides a reproducible, scalable, and secure environment for training and deploying models.
• Feature Store: The Feature Store is a repository of features used to build ML models. It contains a wide variety of features, including text data, images, time series, and SQL tables. In addition, you can use the Feature Store to create custom features or use predefined features.
• AutoML: AutoML is a feature of the Databricks runtime that automates building ML models. It uses a combination of techniques, including automated feature extraction, model selection, and hyperparameter tuning to build optimized models for performance.
• Managed MLflow: MLflow is an open-source platform for managing the ML lifecycle. It provides a common interface for tracking data, models, and runs as well as APIs and toolkits for deploying and monitoring models.
• Model Registry: The Model Registry is a repository of machine learning models. You can use it to store and share models, track versions, and compare models.
• Repos: Allows engineers to follow Git workflows in Databricks. This enables engineers to take advantage of automated CI/CD (continuous integration and continuous delivery) workflows and code portability.
• Explainable AI: Databricks uses Explainable AI to help detect any biases in the model. This ensures your ML models are understandable, trustworthy, and transparent.

Pros

• A unified approach simplifies the data stack and eliminates the data silos that usually separate and complicate data science, business intelligence, data engineering, analytics, and machine learning. 
• Databricks is built on open source and open standards, which maximizes flexibility.
• The platform integrates well with a variety of services.
• Good community support.
• Frequent release of new features.
• User-friendly user interface.

Cons

• Some improvements are needed in the documentation, for example, using MLflow within existing codebases.

Pricing

Databricks offers a 14-day full trial if using your own cloud. There is also the option of a lightweight trial hosted by Databricks.

Pricing is based on compute usage and varies based on your cloud service provider and Geographic region.

Getting Started with MLOPS

MLOps is the future of machine learning, and it brings a host of benefits to organizations looking to deliver high-quality models continuously. It also offers many other benefits to organizations, including improved collaboration between data scientists and developers, faster time-to-market for new models, and increased model accuracy. If you’re looking to get started with MLOps, the tools above are a good place to start.

Also read: Best Machine Learning Software in 2022

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