Manage your Machine Learning Lifecycle with MLflow – Part 1

Machine Learning (ML) is not easy, but creating a good workflow which you can reproduce, revisit and deploy to production is even harder. There has been many advances towards creating a good platform or managing solution for ML. Note that this is not the Data Science (DS) Lifecycle, which is more complex and has many parts. The ML lifecycle exists inside the DS lifecycle. These packages are great, but not so easy to follow.