Thursday, October 28, 2021
When you start exploring multiple model architectures with different hyperparameter values, you need a way to quickly iterate. There are a lot of ways to handle this, but all of them require time and you might not be able to go back to a particular point to resume or restart training.
In this talk, you will learn how you can use the open-source tool, DVC, to compare training metrics using two methods for tuning hyperparameters: grid search and random search. You'll learn how you can save and track the changes in your data, code, and metrics without adding a lot of commits to your Git history. This approach will scale with your data and projects and make sure that your team can reproduce results easily.
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