In the last blog post, we demonstrated the ease with which you can get started with MLflow, an open-source platform to manage machine learning lifecycle. In particular, we illustrated a simple Keras/TensorFlow model using MLflow and PyCharm. This time we explore a binary classification Keras network model. Using MLflow’s Tracking APIs, we will track metrics—accuracy and loss–during training and validation from runs between baseline and experimental models. As before we will use PyCharm and localhost to run all experiments.