AutoML refers to the automation of repetitive tasks in building machine learning or deep learning models. AutoML tries to automate the tasks in the ML pipeline such as data cleaning, feature engineering, handling of categorical features, hyper-parameter tunning with as little manual interaction as possible. The main aim of AutoML is to bring the machine learning tools to non-machine learning or non-technical experts. Databricks AutoML allows us to quickly build machine learning models by automating the tasks such as data preprocessing, feature engineering, hyper-parameter tuning, and best model selection. Databricks AutoML integrates with the MLflow to register the best-performed model to the model registry for model deployment (Serving model over REST API).

Databricks today unveiled a new cloud-based machine learning offering that's designed to give engineer everything they need to build, train, deploy, and manage ML models. The new offering is designed to bridge the gap in existing machine learning products that arises by focusing too much on data engineering, ML model creation, or the deployment aspects of the machine learning cycle, Databricks says. "Many ML platforms fall short because they ignore a key challenge in machine learning: they assume that data are available at high quality and ready for training," Databricks says in its announcement. "That requires data teams to stitch together solutions that are good at data but not AI, with others that are good at AI but not data." To address this gap, Databricks lets users switch between user "experiences" that it exposes, including data science/engineering, SQL analytics, and machine learning experiences, to access tools and features relevant to their everyday workflow.