Introducing MLflow for End-to-End Machine Learning on Databricks

Solving a data science problem is about more than making a model. It entails data cleaning, exploration, modeling and tuning, production deployment, and workflows governing each of these steps. In this simple example, we’ll take a look at how health data can be used to predict life expectancy. It will start with data engineering in Apache Spark, data exploration, model tuning and autologging with hyperopt and MLflow. It will continue with examples of how the model registry governs model promotion, and simple deployment to production with MLflow as a job or REST endpoint. This tutorial will cover the latest innovations from MLflow 1.12.
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