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.
Databricks has a great video on how MLflow fits into the data science process.
In this simple example, we’ll take a look at how health data can be used to predict life expectancy. Starting with data engineering in Apache Spark, data exploration, model tuning and logging 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 dashboard.