Liu, Yong
Practical Deep Learning at Scale with MLflow: Bridge the gap between offline experimentation and online production
- ISBN 13:
- 9781803241333
- author:
- Liu, Yong
- format:
- Paperback
- publisher:
- Packt Publishing Limited
- language:
- English
- Publication Year:
- 2022
- Pages:
- 288
- Dimensions:
- 1.6 x 19.1 x 19.1 centimetres (0
- Genre:
- Computers, Hardware, Computer Engineering,
- Condition:
- New
- Availability:
- Item usually sent within 5 working days
Description
Deep Learning at Scale with MLflow: A Practical Guide This book provides a comprehensive introduction to deep learning models and pipelines at scale, using MLflow as a unified framework for tracking data, code, and pipelines. You'll learn how to develop practical business AI solutions, from experimentation to production, with a focus on reproducibility and provenance awareness. You'll discover how to train, run, tune, and deploy deep learning pipelines, including explainability and reproducibility. The book covers key topics such as running DL pipelines in a distributed environment, tuning models through hyperparameter optimization, and building multi-step inference pipelines. By the end of this guide, you'll have gained the hands-on experience needed to develop a DL pipeline solution from initial offline experimentation to final deployment. With MLflow Bridge, you'll be able to bridge the gap between offline experimentation and online production, ensuring reproducibility and transparency in your deep learning models and pipelines.
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