I have been working with MLflow tools for a few months that's why I decided to show to Data Scientists and ML developer how to leverage MLflow as a platform to track experiments, package projects to reproduce runs, use model flavors to deploy in diverse environments, and manage models in a central respository for sharing. For quick start you can easily clone my github repository to use all notebooks in your own workspace. You should create a workspace using AWS Account. Once it's ready, import first file to your workspace to see full code and try in on your own. Then you need to create a cluster.

Machine learning brings new complexities beyond the traditional software development lifecycle. To address these challenges, Databricks unveiled MLflow, an open source project aimed at simplifying the entire machine learning lifecycle. MLflow allows companies of all sizes to accelerate the machine learning lifecycle by introducing simple abstractions to package reproducible projects, track results, and encapsulate models. Keep track of experiment runs and results across frameworks. Execute projects remotely on to a Databricks cluster, and quickly reproduce your runs.