Experiment tracking is very important steps when it comes to model deployment in production. When we make model ready for deployment we compare the performances of different recorded experiments and check which one is the optimal one. That is where it becomes very much important to understand how to do experiment tracking for different models trained under different machine learning frameworks. In this post I am explaining step by step approach to do experiment tracking for TensorFlow Based image classification model.
Data Science Duniya 