A Review of Azure Automated Machine Learning (AutoML)

Azure AutoML is a cloud-based service that can be used to automate building machine learning pipelines for classification, regression and forecasting tasks. Its goal is not only to tune hyper-parameters of a given model, but also to identify which model to use and how to pre-process the input dataset.

fig. 1 — Azure AutoML workflow taken from the official infographic

AutoML uses Bayesian optimization to identify better hyper-parameters than human experts. It also uses collaborative filtering (Probabilistic Matrix Factorization) to search for the most promising data transformation pipelines efficiently, based on a database that is built by performing millions of different pipeline evaluation experiments on many datasets. This database helps in finding the good solutions for new datasets quickly.

Azure Machine Learning offers two experiences for working with AutoML:

• A simple user interface included into Azure ML Studio for limited/no code experience customers
• The Azure ML Python SDK, a Python library that allows code experience customers and Data Scientists to develop ML models using all the features Azure ML makes available

All features described…