Production-Ready Applied Deep Learning

eBook Details:

• Paperback: 322 pages
• Publisher: WOW! eBook (August 30, 2022)
• Language: English
• ISBN-10: 180324366X
• ISBN-13: 978-1803243665

eBook Description:

Production-Ready Applied Deep Learning: Supercharge your skills for developing powerful deep learning models and distributing them at scale efficiently using cloud services

Machine learning engineers, deep learning specialists, and data engineers without extensive experience encounter various problems when moving their models to a production environment.

Developers will be able to transform models into a desired format and deploy them with a full understanding of tradeoffs and possible alternative approaches. The book provides concrete implementations and associated methodologies that are off-the-shelf allowing readers to apply the knowledge in this book right away without much difficulty.

In this Production-Ready Applied Deep Learning book, you will learn how to construct complex models in PyTorch and TensorFlow deep-learning frameworks. You will acquire knowledge to transform your models from one framework to the other and learn how to tailor them for specific requirements that the deployment setting introduces. By the end of this book, you will fully understand how to convert a PoC-like deep learning model into a ready-to-use version that is suitable for the target production environment.

• Learn how top-tier technology companies carry out a deep learning projects
• Data preparation, model development & deployment, monitoring & maintenance
• Convert a proof-of-concept deep learning model into a production-ready application
• Learn various deep learning libraries like PyTorch / PyTorch Lightning, TensorFlow with and without Keras, TensorFlow with JAX
• Learn techniques like model pruning and quantization, model distillation & model architecture search
• Propose the right system architecture for deploying various AI applications at large scale
• Set up a deep learning pipeline in an efficient and effective way using various AWS services

Readers will have hands-on experience with commonly used deep learning frameworks and popular web services designed for data analytics at scale. You will get to grips with our collective know-hows from deploying hundreds of AI-based services at large scale.

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