How to Use MLflow, TensorFlow, and Keras with PyCharm - The Databricks Blog

At Spark AI Summit in June, we announced MLflow, an open-source platform for the complete machine learning cycle. The platform's philosophy is simple: work with any popular machine learning library; allow machine learning developers experiment with their models, preserve the training environment, parameters, and dependencies, and reproduce their results; and finally deploy, monitor and serve them seamlessly--all in an open manner with limited constraints. In this blog, we will focus on one of the factors: Minimal time to get started. In upcoming blogs, we will elaborate on the other factors, albeit we'll briefly mention them here. Let's consider the level of effort it takes to get started using MLflow in your favorite IDE.