Practical Deep Learning at Scale with MLflow

This guide focuses on managing deep learning models and pipelines using MLflow, emphasizing the importance of reproducibility and provenance awareness. It covers key processes such as training, testing, tracking, and deploying models at scale. Readers will learn how to effectively store and tune models while ensuring that their development and deployment can be easily explained and replicated. This resource is essential for those looking to enhance their machine learning workflows with robust tracking and management techniques.