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Practical Deep Learning at Scale with MLflow

You're reading from  Practical Deep Learning at Scale with MLflow

Product type Book
Published in Jul 2022
Publisher Packt
ISBN-13 9781803241333
Pages 288 pages
Edition 1st Edition
Languages
Author (1):
Yong Liu Yong Liu
Profile icon Yong Liu

Table of Contents (17) Chapters

Preface Section 1 - Deep Learning Challenges and MLflow Prime
Chapter 1: Deep Learning Life Cycle and MLOps Challenges Chapter 2: Getting Started with MLflow for Deep Learning Section 2 –
Tracking a Deep Learning Pipeline at Scale
Chapter 3: Tracking Models, Parameters, and Metrics Chapter 4: Tracking Code and Data Versioning Section 3 –
Running Deep Learning Pipelines at Scale
Chapter 5: Running DL Pipelines in Different Environments Chapter 6: Running Hyperparameter Tuning at Scale Section 4 –
Deploying a Deep Learning Pipeline at Scale
Chapter 7: Multi-Step Deep Learning Inference Pipeline Chapter 8: Deploying a DL Inference Pipeline at Scale Section 5 – Deep Learning Model Explainability at Scale
Chapter 9: Fundamentals of Deep Learning Explainability Chapter 10: Implementing DL Explainability with MLflow Other Books You May Enjoy

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