Automated machine learning


Automated machine learning is the process of automating the process of applying machine learning to real-world problems. AutoML covers the complete pipeline from the raw dataset to the deployable machine learning model. AutoML was proposed as an artificial intelligence-based solution to the ever-growing challenge of applying machine learning. The high degree of automation in AutoML allows non-experts to make use of machine learning models and techniques without requiring to become an expert in this field first.
Automating the process of applying machine learning end-to-end additionally offers the advantages of producing simpler solutions, faster creation of those solutions, and models that often outperform hand-designed models.

Comparison to the standard approach

In a typical machine learning application, practitioners have a set of input data points to train on. The raw data may not be in a form that all algorithms can be applied to it. To make the data amenable for machine learning, an expert may have to apply appropriate data pre-processing, feature engineering, feature extraction, and feature selection methods. After these steps, practitioners must then perform algorithm selection and hyperparameter optimization to maximize the predictive performance of their model. All of these steps induce challenges, accumulating to a significant hurdle to get started with machine learning.
AutoML dramatically simplifies these steps for non-experts.

Targets of automation

Automated machine learning can target various stages of the machine learning process. Steps to automate are: