Summary

Since DL projects involve many iterations of training models and evaluation, efficiently managing experiments, models, and datasets can help the team reach its goal faster. In this chapter, we looked at the two most popular settings for DL project tracking: W&B and MLflow integrated with DVC. Both settings provide built-in support for Keras and PL, which are the two most popular DL frameworks. We have also spent some time describing tools that put more emphasis on dataset versioning: Neptune and Delta Lake. Please keep in mind that you must evaluate each tool thoroughly to select the right tool for your project.

At this point, you are familiar with the frameworks and processes for building a proof of concept and training the necessary DL model. Starting from the next chapter, we will discuss how to scale up by moving individual components of the DL pipeline to the cloud.