Keepsake is a Python library that uploads files and metadata (like hyperparameters) to Amazon S3 or Google Cloud Storage. You can get the data back out using the command-line interface or a notebook. Then Keepsake will start tracking everything: code, hyperparameters, training data, weights, metrics, Python dependencies, and so on. Your experiments are all in one place, with filter and sort. Because the data's stored on S3, you can even see experiments that were run on other machines.

In this blog post, we'll discuss about Keepsake . Keepsake is a version control tool for machine learning experiments. I, myself as a machine learning engineer feel bewildered whenever I need to deploy a ML model in production. I have lot of questions before deploying like how to track the each model and its parameters, how to move back if some thing is screwed so many big and small questions. Now, I think, I found a one good answer for all the problem.