Machine learning (ML), the engine of artificial intelligence (AI), is a complex set of processes that requires highly skilled experts to carry out the successful development of ML models. It is an arduous, costly, repetitive process that, as the need for AIOps in enterprises grows, can become a stumbling block on the path to digital transformation.
In recent years, automated ML (AutoML) has become a tool to simplify steps in the ML process, allowing enterprises that want to introduce AIOps into their operations to do so cheaply. However, aligning data processing with business operations can be daunting. AutoML is increasingly being used to bring the AI-driven automation learning curve down for organizations across all sectors that are invested in working with people both skilled and unskilled in machine learning methodologies—allowing for greater ease in quickly building effective and viable AI models.
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What is AutoML?
AutoML is the automation of common ML modeling processes to allow data scientists and non-experts (also referred to as citizen data scientists) to successfully make ML models. It does this by automatically preparing and cleaning raw data and creates models using the relevant information pulled from that data—becoming a powerful data visualization and model deployment tool.
AutoML is comprised of the following steps:
- Data preparation: Unstructured data is prepared, cleansed and converted into structured data that can be used as a model-training dataset.
- Feature engineering: By analyzing the model-training dataset, autoML creates features that are compatible with ML algorithms.
- Feature extraction: AutoML combines different features to create new features to enable more accurate results and reduce the amount of data being processed.
- Feature selection: AutoML chooses the most useful features to generate a model.
- Algorithm selection: The best-performing model is chosen from among competing models based on a set of metrics.
- Hyperparameter optimization/tuning: Optimal hyperparameters are chosen as the basis for a learning algorithm.
Also read: Adversarial Machine Learning: Combating Data Poisoning
AutoML Model Types
AutoML model types are based on the four data types—tabular (structured data), text, image, and video—that will be analyzed.
- Tabular data: Used to train ML models to make predictions on new data.
- Text data: ML models can be made to analyze the structure and meaning of text using classification, information extraction, and inferred sentiment.
- Image data: ML models to analyze the contents of an image using classifications and object detection.
- Video data: ML models used to classify videos based on a set of parameters, find selected actions, and track specific objects and people.
Benefits of AutoML
As a code-free, automated process, AutoML allows organizations to quickly apply ML to various aspects of their business. It gives citizen data scientists the tools to build, iterate, and deploy models to gain valuable insights that underpin effective decision making, while it frees data scientists from the labor-intensive MLOps cycle. The result is more time and greater focus on model customization and analytics.
Recent research on the adoption of autoML shows what a game changer it is becoming, with the market anticipated to grow from $346.2 million in 2020 to $14,830.8 million by 2030—a CAGR of 45.6% from 2020 to 2030. Enterprises across almost all business sectors are bound to benefit from the implementation of autoML as digital transform initiatives drive the need for more data scientists and experts as well as a reduction in the costs and time spent in creating ML models.
Key benefits of autoML for enterprises include:
- Rapid deployment of ML models: With citizen data scientists empowered to build ML models alongside more experienced data scientists, organizations can more quickly deploy effective and better performing solutions.
- Increased productivity: For data scientists more time can be spent on supervising more complex ML model builds and implementations, including in edge computing and data storage environments.
- Better business analytics: AutoML quickly delivers analytics that can be used in a number of ways, including building better customer experiences, detecting fraud, and managing inventory.
Also read: Top 8 AI and ML Trends to Watch
The Future of AutoML
As the integration of ML and AI continue to evolve enterprises into automated powerhouses, AutoML plays a key role in democratizing the processes needed to sustain that push. With data scientists currently in high demand to help fuel that digital transformation, having AutoML as a tool to train a pool of citizen data scientists in delivering effective solutions to solve a host of old and new problems opens the gates to further both technological and cultural innovations.
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