Managing the Complete Machine Learning Lifecycle with MLflow

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.