Managing Machine Learning Lifecycles with MLflow

Model development and experimentation is part of any machine learning lifecycle. However, without careful planning, keeping track of experiments can become tedious and challenging; especially given the number of configurations we typically deal with. MLflow is a machine learning lifecycle framework that allows ML engineers and teams to keep track of their experiments. In PART 1 of the series, we are going to focus on the first two steps -- tracking experiments and sharing code. PART 2 will be dedicated to model packaging, while PART 3 will show how the concepts outlined in the previous parts can be used in a React web application. For now, let's try to understand what MLflow is, and what it can do for us!