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Yiqiao Yin Oct 09, 2022

Machine learning engineers, deep learning specialists, and data engineers encounter various problems when moving deep learning models to a production environment. The main objective of this book is to close the gap between theory and applications by providing a thorough explanation of how to transform various models for deployment and efficiently distribute them with a full understanding of the alternatives.✅Learn how to construct complex deep learning models in PyTorch and TensorFlow✅Acquire the knowledge you need to transform your models from one framework to the other and learn how to tailor them for specific requirements that deployment environments introduce✅Get hands-on experience with commonly used deep learning frameworks and popular cloud services

Amazon Verified review

Steven Fernandes Oct 18, 2022

The book is a good introduction book for learning the production steps of deep learning. The book helps a beginner to understand various available frameworks for deploying deep learning models. After introducing the initial basic concepts, the end of part 1 introduces well-known deep learning project tracking using Weights & Biases, MLflow and DVC.Part 2 covers data preparation and model training using Horovod, Ray, Kubeflow, and Sagemaker. The explainable AI section at the end of part 2 could have been presented better. Part 3, deployment and maintenance, helps beginners to get an overall idea of Open Neural Network Exchange (ONNX), and Elastic Kubernetes Services. However, certain sections of the chapter could have been better. For example, chapter 11, Deep Learning on Mobile Devices, doesn't explain the detailed steps to deploy the model on iOS and Android apps. The GitHub section for chapter 11 are links that redirect us to TensorFlow lite. Overall a good introduction book to know how production can be done of deep learning models.

Bill C Richmond Dec 23, 2022

Looking at the book’s table of contents should tell you if this is the book for you or not. I find many books today focus on theory rather than getting your hands dirty. This one is the later. With loads of links, supplemental material on GitHub, code examples, and explanations, this book is for both beginners and experts. The focus on relevant tools (AWS, PyTorch, Tensorflow, SageMaker, W&B, MLFlow, Kubernetes, ONNX, etc.) is really good. My team uses most of these tools, have to convert between frameworks, use MLOps pipelines, etc., and the authors’ explanation was spot on. It’s a solid read but also a useful reference. Overall, one of the best books I’ve seen on the topic and highly recommended.

Anup Sep 27, 2022

It's is good for data/ML engineers to quickly learn how to productionize and support DL pipelines.

Dror Oct 29, 2022

This is a wonderful and rather unique book on deep learning (DL) in production. In contrast to most available books on machine learning (ML) that cover mostly theory and/or model training, this book focuses on real-world aspects of model deployment and DL pipelines in production. It covers a variety of important (and often neglected) topics, including data preparation, model management and experiment tracking (with W&B and DVC), as well as production-related topics such as model deployment and monitoring in cloud (AWS) and mobile (iOS and Android) environments. Deep learning frameworks covered include both PyTorch and TensorFlow.I can imagine two main audiences that will benefit the most from this book: software engineers that will learn how to apply their knowledge to build AI-focused applications, and machine learning practitioners and data scientists that will learn what it takes to productionize their models and turn them into customer-facing applications.While this book should probably not be your first experience with DL, if you already have some knowledge on DL or are a software engineer focusing on DL in production, this book will take your knowledge to the next level and make you a better DL practitioner, well-versed in the different aspects of DL in production.

Production-Ready Applied Deep Learning: Learn how to construct and deploy complex models in PyTorch and TensorFlow deep learning frameworks

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Production-Ready Applied Deep Learning: Learn how to construct and deploy complex models in PyTorch and TensorFlow deep learning frameworks

What do you get with a Packt Subscription?

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Description

Who is this book for?

What you will learn

Product Details

What do you get with a Packt Subscription?

Product Details

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About the 3 authors

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