Automate Machine Learning using Databricks AutoML -- A Glass Box Approach and MLFLow

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).