Practical Deep Learning at Scale with MLflow

Bridge the gap between offline experimentation and online production

Autori

• Yong Liu

Parametri

• 288 pagine
• 11 ore di lettura

• Computer, IT, programmazione

Maggiori informazioni sul libro

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.

Lingua: Inglese

Pubblicato: 2022

(In brossura)