TIBCO, during TIBCO NOW 2021 online conference, also revealed it has expanded its existing portfolio to data analytics and integration platforms to include a TIBCO Spotfire Mods framework that makes it easier to create visualizations for its Spotfire analytics tool. 

There is also now a Spotfire Data Functions capability that extends the Mods framework that integrates Spotfire with other data sources. TIBCO is also promising to integrate Spotfire with TIBCO ModelOps, a no-code tool it provides for building models that infuse processes with artificial intelligence (AI) capabilities.

Meanwhile, the TIBCO WebFOCUS business intelligence (BI) tool can now be deployed as a containerized application. The company is also making available an instance of TIBCO WebFocus available as a managed service through which it manages the BI application on behalf of customers. There are also now enhanced authoring and assembly capabilities such as filtering, styling, reporting, and app development that end users can employ to augment data preparation, content creation, and collaboration.

Adding Data Sources

TIBCO is also adding support for additional data sources such as Spofire, Apache Spark, and AutoML frameworks along with integration with TIBCO Data Virtualization to the TIBCO Data Science platform in addition to adding a Dynamic Learning capability to the TIBCO Streaming platform that enable real-time analytics.

The TIBCO Data Virtualization platform, now part of the TIBCO Data Quality (DQ) series of offerings, has been updated to add a search capability to its catalog along with making available low-code tools to data engineers. TIBCO EBX master data management platform, part of the same family of tools, now also supports low-code scripting, simplified configuration, smart match and merge of data processes, and integration with additional application programming interfaces (APIs). 

The company has also updated TIBCO Cloud to add lifecycle management capabilities for APIs enabled by its Mashery platform, support for messages sent from platforms such as Apache Kafka and Apache Pulsar, and additional automated workflows.

Finally, TIBCO has added additional projects to a TIBCO LABS initiative through which it collaboratively develops intellectual property with partners and customers. TIBCO Cloud Discover is a process mining tool that will become part of TIBCO Cloud, while TIBCO Cloud Composer is a rapid application development platform. There’s also TIBCO LABS Gallery, a portal for accessing these projects.

Also read: Top Data Science Tools 2021

Driving Digital Transformation

The overall goal is to make it easier for organizations to drive digital transformation initiatives using platforms that are provided by a single vendor, says TIBCO COO Matt Quinn. Rather than having to navigate multiple silos, Quinn notes that as digital transformation continues to gain momentum there will be more convergence across disparate IT platforms that today are typically acquired and managed in isolation from one another.

At present, that level of complexity tends to provide larger organizations with a strategic advantage because they have the expertise required to master all the platforms needed to digitally transform a process, adds Quinn. However, as more technologies start to converge it should become easier for small companies to take advantage of the same capabilities, he notes. “There is still a digital divide,” says Quinn.

It may be a while before that digital divide is bridged. However, once it is, many larger companies may soon find themselves competing with smaller agile companies that can employ the same rich mix of platforms to compete more aggressively than ever.

Read next: Best Data Governance Tools & Software of 2021

The post Vista, Blue Prism Deal Bets on Digital Transformation Convergence appeared first on IT Business Edge.

At the same time, AWS has enhanced the S3 cloud storage service based on an object storage system to make it easier to deploy applications that span multiple regions. The cloud service provider has also improved upload/download performance for the S3 service by up to 60% and revamped the Amazon S3 Intelligent Tiering service to remove the minimum duration requirement for all objects stored and removed fees for objects smaller than 128 KB.

AWS has added an Amazon Elastic File System Intelligent Tiering capability that makes it easier for customers to optimize costs for storing data on different tiers of services when access patterns for data change and an AWS Transfer Managed Workflows feature that reduces the manual tasks required for preprocessing data for file transfers

Finally, an audit manager tool for AWS Backup Audit Manager and support for 64TB snapshots when employing the Amazon Elastic Block Store (EBS) service.

Providing Managed Services

When it comes to storage, AWS is best known for its S3 service. The cloud service provider has been working closely with NetApp to enable files to be stored on its cloud. The managed service now extends that effort to include a managed service through which the NetApp ONTAP file system is managed by AWS on behalf of customers. The goal is to make it easier for organizations to shift applications that depend on accessing files to the cloud using NetApp ONTAP, the open source Lustre file system, or the Windows file system, says Edward Naim, general manager, Amazon FSx.

File systems are widely employed in on-premises IT environments and in many ways make it easier to organize data than an object storage system that typically scales higher. “File systems have some advantages over object,” says Naim.  

In general, NetApp remains committed to providing access to ONTAP regardless of where IT teams store files, says Anthony Lye, executive vice president and general manager for public cloud at NetApp.

Also read: Edge AI: The Future of Artificial Intelligence and Edge Computing

Use Cases for OnTAP

In addition to on-premises IT environments that deploy NetApp storage systems, the ONTAP file system can be deployed in the cloud or at the network edge to store data as close as possible to the applications that consume that data via the NetApp application programming interface (API).

Use cases will span everything from intelligent tiering and cloud bursting from on-premises IT environments to the building of data fabrics spanning a heterogeneous IT environment, adds Lye. “Anything that writes to the NetApp API will be compatible,” he says.

Most new cloud applications are employing object storage, but the bulk of existing applications that are being lifted and shifted to the cloud require access to a file system such as NetApp ONTAP. It’s not clear yet if object storage will one day eliminate the need for file systems. However, if that day were to arrive it’s still years away. The challenge now is finding the best way to manage all of the different classes of storage services that applications now routinely employ in a way that best preserves the sanity of the IT organization.

Read next: Implementing IT Service Management Changes with CloudOps

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The goal is to make it simpler for organizations to standardize processes that have been dramatically altered since the start of the COVID-19 pandemic, says Dave Wright, chief innovation officer for ServiceNow. As those processes have evolved in the wake of the pandemic it’s been challenging for organizations to make sure everyone in the organization knows how they work. “There’s a lot of new tribal knowledge that not everybody knows,” he notes.

Improving Employee Experience

Streamlining organizational processes has become a higher priority at a time when many employees are realizing they can not only work from anywhere, but also for anybody. Approximately 3.6 million Americans quit their jobs in May 2021 alone, a monthly rate that has been increasing sharply since the beginning of the year. A major factor in employee retention alongside compensation is, of course, the everyday work experience.

ServiceNow as part of that effort is also adding an Automation Discovery capability to identify the top ten opportunities for automating work across more than 180 topics using ServiceNow applications. In addition, the company has made available Customer Service Playbooks that guide organizations through how to optimize those processes.

ServiceNow is also adding Mobile App Builder, a set of tools that allow developers to rapidly build and configure mobile applications for iOS and Android devices via a single interface.

Finally, enhancements to the Health Log Analytics capability provided by ServiceNow employ machine learning algorithms to predict IT issues. Those enhancements leverage the artificial intelligence for IT operations (AIOps) technologies that ServiceNow gained with its acquisition of Loom Systems earlier this year.

Also read: Best BI Tools 2021: Business Intelligence Software

Building IT Management on SaaS

At the core of the ServiceNow strategy is an effort to enable organizations to use a single software-as-a-service (SaaS) platform to manage both IT operations and workflows involving everyone from human resources to sales and marketing. The goal is to reduce the total cost of implementing a wide range of processes using a platform that many IT teams already employ to manage operations, as opposed to having separate platforms for each type of workflow process.

It’s not clear to what degree organizations are embracing that philosophy but ServiceNow expects that as digital business transformation becomes the dominant business issue of the decade more organizations will look to standardize on a single platform that makes it easier to integrate processes at a lower total cost.

The challenge is that many departments within organizations tend to make their own IT platform decisions. Over time, however, each of those platforms needs to be integrated, which is a task often left to IT departments to thanklessly figure out in a way that is usually more expensive than most everyone involved appreciates.

Read next: Democratizing Software Development with Low-Code

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The trouble is most developers have little to no security expertise. Cloud applications are today rife with misconfigurations that cybercriminals regularly exploit. It’s not uncommon for organizations to discover sensitive data has been exfiltrated from a cloud computing environment because a developer left a port open.

This issue has resulted in the rise of a set of best DevSecOps practices through which developers will be provided with both the education, tools, and processes needed to build more secure applications. The challenge is that it takes time to institute that level of cultural change among developers. As a result, IT organizations are turning to a new class of Cloud-Native Application Protection (CNAP) platforms that continuously scan workloads and configurations both during the application development process and after the application has been deployed in a production environment.

What is CNAP?

As a category of security platforms coined by Gartner, a CNAP platform at its core aggregates two types of security platforms. Cloud Security Posture Management (CSPM) platforms are already employed by many organizations to surface misconfigurations and other vulnerabilities that cybercriminals could potentially exploit. The second core capability comes in the form of some type of Cloud Workload Protection Platforms (CWPP) that makes use of agent software to protect a workload running on either a virtual machine or encapsulated in a container.

The degree to which any application workload is cloud native is open to interpretation. In the case of CNAP, the security policies can be applied to any workload regardless of whether it is a microservices-based application built using containers or a legacy monolithic application running on top of virtual machines. The term cloud-native is meant to simply describe the fact that the security platform deployed on a cloud platform is built on top of a modern IT framework such as the Kubernetes container orchestration engine.

Read more about CNAP, CSPM, and CWPP: 6 Cloud Security Must-Haves – with Help from CSPM, CWPP or CNAPP

Centralizing Security Management

Regardless of terminology employed, the need to centralize the management of security, especially in cloud computing environments, is becoming more apparent as the rate at which applications are being developed and deployed continues to increase. Theoretically, of course, organizations could opt to slow the rate at which applications are being developed and deployed to conduct more security reviews. In practice, however, organizations are looking for ways to make sure applications are more secure without materially impacting the rate at which they are built. As more organizations realize how dependent they are on new applications and subsequent updates to drive revenue in the age of digital business transformation the less inclined they are to slow down application development.

The paradox is the faster applications are deployed the larger the attack surface that needs to be defended becomes. Many of those applications are now driving mission-critical digital processes that revolve around highly sensitive data. In the absence of any willingness to slow down the rate at which applications are being deployed the next best thing is to increase the ability of cybersecurity teams to keep pace with that level of innovation. CNAP platforms promise to unify security processes in a way that instruments applications by making use of agent software to enforce security policies and then continuously scanning application environments for any violation of the policies defined by the cybersecurity team. That approach essentially relies on automation to place guardrails around the cloud platform developers are employing to build and deploy their applications.

Building a Unified CNAP Platform

Naturally, the race is now on to integrate CSPM and CWPP offerings to create a unified CNAP Platform. It’s still early days so IT organizations should expect to see a raft of CNAP platform launches in the week and months ahead. In many cases, vendors are simply responding to a larger trend toward consolidation of security management that is already underway. A recent survey of 383 IT and security professionals  conducted by Enterprise Strategy Group (ESG) on behalf of Lacework, a provider of an IT security platform, finds more than a third (35%) have already consolidated security controls, with another 50% reporting they are moving in that direction over the next two years. In that sense, CNAP is only the latest manifestation of an ongoing convergence of a wide range of security functions that is being driven by the need to reduce the total cost of security even as application environments become more complex.      

There will never be any such thing as perfect security any time soon. However, many organizations have concluded that when it comes to security they are often their own worst enemy. The challenge and the opportunity now is to modernize security platforms in a way  that allows both developers and security teams to accomplish their core missions without continually having to get in each other’s way.

Read next: Detecting Vulnerabilities in Cloud-Native Architectures

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Data stored in the OS-Climate Data Commons platform built for the non-profit OS-Climate organization will be made accessible via open source analytics tools IT and made interoperable with tooling that organizations already use to manage  workflows and internal processes. That effort is intended to provide a portable platform for managing complex data ingestion and processing flows using the latest advances in machine learning algorithms to collate and sanitize public and proprietary data sources. This process should make it easier for organizations to achieve compliance mandates by more accurately aggregating corporate climate and environmental, social, and governance (ESG) metrics.

Other members of the non-profit OS-Climate organization include Allianz, Amazon, BNP Paribas, Goldman Sachs, KPMG, Microsoft and the Net Zero Asset Owner Alliance.

Also read: Data Management with AI: Making Big Data Manageable

Climate Change’s Impact on IT Processes

In addition to creating new compliance requirements, climate change issues are also starting to have a significant impact on organizations that are evaluating how much carbon they are generating. Regardless of how much anyone in IT might subscribe to the science of climate change, organizations are starting to factor it into IT decisions that might result in the shifting of more workloads to the cloud to reduce carbon emissions.

IT, of course, is only a small part of the overall climate change equation. However, organizations are attempting to reduce the amount of carbon they generate to qualify for financial incentives that governments around the world are starting to make available. There are also penalties that require organizations to buy carbon credits from other organizations, some of which may even be rivals.

Achieving Net-Zero Carbon Emissions

The overall goal of the Red Hat effort is to make it make easier for organizations that rely extensively on IT to achieve net-zero carbon emissions goals they have set for themselves in keeping with the mandates created by the Paris Climate Accords, say Kelly Switt, senior director for financial services industry strategy, ecosystem and strategic partnerships at Red Hat. As part of that effort, it needs to become simpler for organizations to determine what action they might take that will make a meaningful difference, adds Switt. “Otherwise, there tends to be this paralysis of analysis,” she says.

Organizations across every major vertical industry sector will eventually need to come to terms with climate change regulatory requirements. The initial challenge most of them will encounter is simply finding reliable data upon which they can build a strategy to reduce their carbon footprint. An open source platform that reduces the total cost of collecting that data may very well turn out to be the first step toward achieving that goal in a way that doesn’t cost more than the organization might ultimately save.

Read next: Steps to Improving Your Data Architecture

The post Red Hat to Build Analytics Platform to Help Combat Climate Change appeared first on IT Business Edge.

VMware software will come pre-loaded on a pair of ruggedized and security enhanced ThinkSystem SE350 servers based on Intel Xeon processors that can be managed via Lenovo Open Cloud Automation management software that previously was limited to managing servers in a data center. 

The goal is to enable IT teams to manage highly distributed computing environments using the same software many of them already employ to manage IT infrastructure, says Charles Ferland, vice president and general manager for edge computing and communication service providers for the Infrastructure Solutions Group (ISG) at Lenovo.

The 1u ThinkSystem SE350 servers are designed to be deployed across a wide range of edge computing use cases. Lenovo has also made it possible to employ a number of security measures to make sure those servers are not compromised, including encrypting data and locking down the system should anyone tamper with it or outright steal the entire system, notes Ferland. “They can steal the device but not the data,” he says.

It’s up to each organization to determine whether they want to acquire those Lenovo platforms using either a traditional capital expense model or make use of an operating expense model that uses metering to determine what monthly rate to charge, notes Ferland.

Also read: Edge AI: The Future of Artificial Intelligence and Edge Computing 

Keeping up with the Edge

In many instances, the adoption of edge computing platforms is already running ahead of the ability of IT organizations to manage and secure them as organizations seek to process and analyze data closer to the point where it is being created and consumed. That approach then allows IT teams to reduce the amount of raw data that would otherwise have to be transferred across a wide area network (WAN). Lenovo is making a case for achieving that goal as part of an integrated edge computing platform that can be remotely managed using a control plane it provides. The platform is based on the open source Ansible automation framework and extensions that Lenovo created using JavaScript Object Notation (JSON) files.

There may soon come a day when there are more workloads running outside a traditional data center or cloud computing environment than in it. Gartner predicts that by 2025, some 75% of enterprise-generated data will be created and processed outside of the traditional data center or cloud.  The 2021 State of the Edge report by the Linux Foundation predicts that the global market capitalization of edge computing infrastructure would be valued at more than $800 billion by 2028.

One way or another, enterprise IT environments are becoming more extended by the day. The challenge is that the attack surface is also extending and that needs to be defended by IT operations teams that are already struggling to manage IT infrastructure already deployed in on-premises and in the cloud.    

Read next: Edge Computing Emerges as Next Big Cybersecurity Challenge

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At the same time, Salesforce has added a sales enablement module that makes it simpler to onboard and train sales staff by leveraging Einstein AI to enable sales leaders to identify issues based on metrics such as lead-to-opportunity conversion rates, sales cycle times, and the rate at which deals close.

Finally, Salesforce has also added a Subscription Management for Revenue Cloud module that makes it easier to manage sales teams that are being incentivized to drive recurring revenues.

Providing Actionable Intelligence

Salesforce has been steadily increasing the amount of AI that can be brought to bear on sales management processes for several years now. The Einstein Deal updates are designed to make it easier for sales leaders to spend less time collecting metrics that can be more easily aggregated by the Einstein AI platform, says Taksino Eammano, senior vice president of product for Sales Cloud at Salesforce.

The goal is to provide more actionable intelligence to identify not only what deals are likely to close, but also which ones based on key performance indicators (KPIs) surfaced using Tableau analytics software might close sooner if additional efforts were made, she adds. 

The Sales Enablement module, meanwhile, can be employed to identify areas where additional training provided within the context of a specific deal could also accelerate sales, notes Eammano.

At a time when many sales teams are operating out of their homes it’s become more challenging for sales leaders to identify issues members of their sales teams may be encountering. A command center provides a fact-based approach to managing sales teams that leverages AI and analytics reduces the need for sales leaders to rely on guesswork and intuition, says Eammano.

That’s critical because as business models evolve the age of digital transformation there’s more uncertainty than ever, Eammano adds. “There’s a digital imperative.” 

Also read: AI and Observability Platforms to Alter DevOps Economics

Taking Advantage of AI Now

Of course, no amount of software is ever going to close a deal. Most people still buy anything of any real consequence from someone they have engaged with despite the rise of e-commerce as a vehicle for processing the ultimate transaction. That’s even more true of business-to-business (B2B) transactions that typically require a lot of effort to first close and then support.

There will come a time soon when every sales representative has their AI assistant that will identify potential opportunities as well as issues that might have a negative impact on customer satisfaction. In the meantime, sales leaders that can’t be everywhere may have no choice but to rely more on AI to help keep revenue flowing. After all, the finance department will most certainly also be applying AI to determine exactly why sales goals aren’t being met. As such, it’s always better to be the one bearing the news first, good or bad, than to be surprised by it when delivered by somebody else.

Read next: Bad Bots & Online Retail: A Q&A With DataDome

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The shift to working from home and accessing applications in the cloud coupled with the rise of edge computing platforms and microservices-based applications that are being deployed across an extended enterprise is increasing the dependency organizations have on network connectivity, says Sudip Datta, general manager and head of AIOps, observability and automation at Broadcom. “There’s now a huge reliance on connectivity,” he says.

The latest version of DX NetOps network monitoring software from Broadcom addresses those requirements by expanding its ability to support more than 500,000 hyper-scale devices alongside more than 300,000 software-defined wide area network (SD-WAN) tunnels. In addition, DX NetOps now supports VMware SD-WAN by VeloCloud. Nokia Nuage software-defined data center, and Cisco Meraki Cloud-Managed LAN and Wi-Fi platforms.

IT teams can also now take advantage of Syslog events/alarms to isolate network faults and improve meantime to recovery, in addition to accessing real-time insights into network performance and automatically generating reports that identify network configuration policy violations. Broadcom has also further centralized security configuration and administration in addition to streamlining the number of alerts that might be generated by network events.

SD-WAN Use Grows with Remote Work

Network management has become a lot more challenging since the COVID-19 pandemic for a wide variety of reasons. More organizations have embraced SD-WANs to provide access to home offices that, from a networking management perspective, represent a massive increase in the number of remote offices requiring connectivity. More end users than ever are also accessing cloud applications over a variety of wide area networks (WANs) 

At the same time, organizations are deploying a new generation of distributed applications based on microservices that tend to be latency sensitive. As such, disruptions to network services can more easily adversely impact application performance.

Finally, edge computing platforms that are being deployed closer to the point where data is being created and consumed also require network access.

Also read: Work-From-Anywhere Shift Will Increase IT Costs

Network Challenges Hamper Efficiency

Unfortunately, networking is often taken for granted until organizations start to realize that, not only are many of the legacy switches and routers they have in place not able to keep pace with demands for bandwidth, they also lack the tools required to manage an underlying network that is becoming more fragmented by the day. Network operations teams, for example, require a tool that enables them to more efficiently manage both network flows and overall performance, says Datta. Today many networking teams are required to navigate multiple tools to achieve that goal, he adds.

Each organization will encounter more complex networking challenges at varying rates. However, with many employees continuing to work from home well into 2022 it’s now more a question of when rather than if IT teams will have to revisit network management across the enterprise. In the absence of that effort, however, the reason why one application experience is so different from another is likely to remain a mystery. 

Read next: Networking’s Future is in the Distributed Cloud

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The Cradlepoint E3000 Series 5G Enterprise Router supports both 5G and Wi-Fi 6 wireless and fiber, and 2.5 gigabit-per-second Ethernet networks. It supports multiple routing protocols as well as IPSEC and DMVPN for security. Other capabilities include support for wireless software-defined wide area networks (SD-WANs) and Cellular Intelligence, a set of tools for tracking usages of data plans.

Finally, Cradlepoint integrates application-level controls, intrusion prevention system (IPS) and intrusion detection system (IDS), IP reputation, web content filtering, and a firewall to microsegment connections.

The Cradlepoint E3000 Series 5G Enterprise Router is designed to make it simpler for organizations to start introducing 5G into their networking environments alongside existing protocols, says Donna Johnson, vice president of product marketing for Cradlepoint. “A lot of organizations are still setting up proof-of-concepts (PoCs),” she says.

Also read: Intel Makes Building 5G Platforms at the Edge Easier

Challenges of Bringing 5G to Enterprises 

It’s still early days as far as 5G networks are concerned in the enterprise. In many cases, organizations are initially adding 5G services to ensure networking services remain available in the event of an outage to their primary network, Johnson added. There are also use cases involving, for example, public safety that may require video to be captured from a location that doesn’t lend itself to the setting up of a wired network. 

Many organizations are also making plans for 5G on the assumption that millimeter waves ranging from 24 GHz to 100 GHz will soon become available to drive a wide range of edge computing use cases. A recent report from International Data Corp (IDC) forecasts the market for enterprise wireless routers to reach $2.98 billion by 2024  

In the meantime, individual end users are already starting to rely more on 5G connections provided by carriers to access corporate applications via a variety of mobile computing devices, including PCs that come with built-in 5G connections. End users are also determining, in many cases, that those 5G connections made via hotspot software on a 5G smartphone are just as fast as a Wi-Fi connection provided either via a hotel or café and are much more secure. 

In other cases, employees are making use of antennas provided by Cradlepoint to connect laptops to 5G wireless services, says Johnson. Employees now working from anywhere to help contain the spread of the COVID-19 pandemic are increasingly making greater use of 5G connections, she adds.

Replacing Wi-Fi Connections with 5G

As usage of 5G steadily increases it’s becoming apparent that, after years of hype, the switchover to 5G is starting to steadily occur. It may still be a while before 5G is the primary means for connecting a remote office to the corporate network. At this point, however, as the cost of making those 5G connections continues to decline it’s now a matter of time before many organizations replace Wi-Fi connections with 5G connections to not only connect with corporate headquarters but to also soon share files among employees working in that office to reduce the number of types of networks an IT team needs to support.

Read next: Edge Computing Set to Explode Alongside Rise of 5G

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The NVIDIA AI Enterprise platform is designed to be deployed on instances of VMware deployed on servers from Atos, Dell Technologies, GIGABYTE, Hewlett Packard Enterprise (HPE), Inspur, Lenovo, and Supermicro that have all been certified by NVIDIA. In addition, Dell announced that Dell EMC VxRail has become the first hyperconverged platform to be certified to run NVIDIA AI Enterprise.

At the same time, Domino Data Lab revealed that its machine learning operations (MLOps) platform for managing the development of AI models has also been certified compatible with NVIDIA AI Enterprise.

While most AI models are trained in the cloud because of the cost of acquiring platforms based on graphical processor units (GPUs), there comes a time when the amount of data being processed to train an AI model makes it more cost-effective to deploy servers in an on-premises IT environment. The monthly costs associated with consuming GPUs via a cloud service quickly adds up, notes Manuvir Das, head of enterprise computing at NVIDIA.

Subscription licenses for NVIDIA AI Enterprise start at $2,000 per CPU socket for one year and include Business Standard Support. Perpetual licenses are $3,595 and require additional support purchase. Customers can also upgrade to Business Critical Support that is available on a 24×7 basis. Even though the platforms are based on GPUs, NVIDIA is licensing its software using a per CPU socket basis. “It’s a pricing model that enterprises are familiar with,” says Das.

It’s not clear at what precise point it makes more economic sense to train AI models in an on-premises IT environment. NVIDIA to lower the barrier for entry for training AI models recently allied with Equinix to make NVIDIA supercomputers for training AI models available for rent via a monthly subscription that starts at $90,000.

Also read: NVIDIA Looks to Lower AI Barrier to Entry

Reducing AI Modelling Costs

No matter how the training of AI models gets sliced up it’s expensive to build and maintain AI models. The good news is the amount of data required to train AI models continues to decline. However, just about every application at some point is going to need to incorporate an AI model, so the number of AI models that need to be trained, maintained and updated is only going to exponentially increase. Right now, many organizations over a year consider themselves lucky to deploy a handful of AI models in production environments.

The pressure to automate the building of AI models in ways that are not dependent on manual processes that require a team of data scientists and engineers is building. In fact, vendors such as GitLab are making a case treating AI models much like any other software artifact that should be managed within the context of a larger set of best DevOps practices.

Each IT team will need to decide how best to go about managing the building and deployment of AI models for themselves but the one thing that is certain is there will soon be a lot more of them floating around the enterprise than anyone might have initially imagined.

Read next: NVIDIA, VMware Create the AI-Ready Enterprise Platform at Cloud Scale

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