Chapter 3: Tracking Models, Parameters, and Metrics

Given that MLflow can support multiple scenarios through the life cycle of DL models, it is common to use MLflow's capabilities incrementally. Usually, people start with MLflow tracking since it is easy to use and can handle many scenarios for reproducibility, provenance tracking, and auditing purposes. In addition, tracking the history of a model from cradle to sunset not only goes beyond the data science experiment management domain but is also important for model governance in the enterprise, where business and regulatory risks need to be managed for using models in production. While the precise business values of tracking models in production are still evolving, the need for tracking a model's entire life cycle is unquestionable and growing. For us to be able to do this, we will begin this chapter by setting up a full-fledged local MLflow tracking server.

We will then take a deep dive into how we can track a model...