Learn TensorFlow Enterprise

Build, manage, and scale machine learning workloads seamlessly using Google's TensorFlow Enterprise

KC Tung 지음

Packt(GCO Science) 2020년 11월 20일 출간

작품소개

책소개

Use TensorFlow Enterprise with other GCP services to improve the speed and efficiency of machine learning pipelines for reliable and stable enterprise-level deployment

▶What You Will Learn
？Discover how to set up a GCP TensorFlow Enterprise cloud instance and environment
？Handle and format raw data that can be consumed by the TensorFlow model training process
？Develop ML models and leverage prebuilt models using the TensorFlow Enterprise API
？Use distributed training strategies and implement hyperparameter tuning to scale and improve your model training experiments
？Scale the training process by using GPU and TPU clusters
？Adopt the latest model optimization techniques and deployment methodologies to improve model efficiency

▶Key Features
？Build scalable, seamless, and enterprise-ready cloud-based machine learning applications using TensorFlow Enterprise
？Discover how to accelerate the machine learning development life cycle using enterprise-grade services
？Manage Google's cloud services to scale and optimize AI models in production

▶Who This Book Is For
This book is for data scientists, machine learning developers or engineers, and cloud practitioners who want to learn and implement various services and features offered by TensorFlow Enterprise from scratch. Basic knowledge of the machine learning development process will be useful.

목차

▶TABLE of CONTENTS
▷ Section 1 ？ TensorFlow Enterprise Services and Features
？Chapter 1: Overview of TensorFlow Enterprise
？Chapter 2: Running TensorFlow Enterprise in Google AI Platform

▷ Section 2 ？ Data Preprocessing and Modeling
？Chapter 3: Data Preparation and Manipulation Techniques
？Chapter 4: Reusable Models and Scalable Data Pipelines

▷ Section 3 ？ Scaling and Tuning ML Works
？Chapter 5: Training at Scale
？Chapter 6: Hyperparameter Tuning

▷ Section 4 ？ Model Optimization and Deployment
？Chapter 7: Model Optimization
？Chapter 8: Best Practices for Model Training and Performance
？Chapter 9: Serving a TensorFlow Model

책 속으로

▶What this book covers
？ Chapter 1, Overview of TensorFlow Enterprise, illustrates how to set up and run TensorFlow Enterprise in a Google Cloud Platform (GCP) environment. This will give you initial hands-on experience in seeing how TensorFlow Enterprise integrates with other data services in GCP.

？ Chapter 2, Running TensorFlow Enterprise in Google AI Platform, describes how to use GCP to set up and run TensorFlow Enterprise. As a differentiated TensorFlow distribution, TensorFlow Enterprise can be found on several (but not all) GCP platforms. It is important to use these platforms in order to ensure that the correct distribution is provisioned.

？ Chapter 3, Data Preparation and Manipulation Techniques, illustrates how to deal with raw data and format it to uniquely suit consumption by a TensorFlow model training process. We will look at a number of essential TensorFlow Enterprise APIs that convert raw data into Protobuf format for efficient streaming, which is a recommended workflow for feeding data into a training process.

？ Chapter 4, Reusable Models and Scalable Data Pipelines, describes the different ways in which a TensorFlow Enterprise model may be built or reused. These options provide the flexibility to suit different situational requirements for building, training, and deploying TensorFlow models. Equipped with this knowledge, you will be able to make informed choices and understand the trade-offs among different model development strategies.

？ Chapter 5, Training at Scale, illustrates the use of TensorFlow Enterprise distributed training strategies to scale your model training to a cluster (either GPU or TPU). This will enable you to build a model development and training process that is robust and take advantage of all the hardware at your disposal.

？ Chapter 6, Hyperparameter Tuning, focuses on hyperparameter tuning as this is a necessary part of model training, especially when building your own model. TensorFlow Enterprise now provides high-level APIs for advanced hyperparameter space search algorithms. Through this chapter, you will learn how to leverage the distributed computing power at your disposal to reduce the training time required for hyperparameter tuning.

？ Chapter 7, Model Optimization, explores the concept of how lean and mean your model is. Does your model run as efficiently as possible? If your use case requires the model to run with limited resources (memory, model size, or data type), such as in the case of edge or mobile devices, then it's time to consider model runtime optimization. This chapter discusses the latest means of model optimization through the TensorFlow Lite framework. After this chapter, you will be able to optimize a trained TensorFlow Enterprise model to be as lightweight as possible for inferencing.

？ Chapter 8, Best Practices for Model Training and Performance, focuses on two aspects of model training that are universal: data ingestion and overfitting. First, it is necessary to build a data ingestion pipeline that works regardless of the size and complexity of the training data. In this chapter, best practices and recommendations for using TensorFlow Enterprise data preprocessing pipelines are explained and demonstrated. Second, in dealing with overfitting, standard practices of regularization as well as some recently released regularizations by the TensorFlow team are discussed.

？ Chapter 9, Serving a TensorFlow Model, describes the fundamentals of model inferencing as a web service. You will learn how to serve a TensorFlow model using TensorFlow Serving by building a Docker image of the model. In this chapter, you will begin by learning how to make use of saved models in your local environment first. Then you will build a Docker image of the model using TensorFlow Serving as the base image. Finally, you will serve this model as a web service through the RESTful API exposed by your Docker container.

출판사 서평

▶ Preface
TensorFlow as a machine learning (ML) library has matured into a production-ready ecosystem. This beginner's book uses practical examples to enable you to build and deploy TensorFlow models using optimal settings that ensure long-term support without having to worry about library deprecation or being left behind when it comes to bug fixes or workarounds.

The book begins by showing you how to refine your TensorFlow project and set it up for enterprise-level deployment. You'll then learn how to choose a future-proof version of TensorFlow. As you advance, you'll find out how to build and deploy models in a robust and stable environment by following recommended practices made available in TensorFlow Enterprise. This book also teaches you how to manage your services better and enhance the performance and reliability of your artificial intelligence (AI) applications. You'll discover how to use various enterprise-ready services to accelerate your ML and AI workflows on Google Cloud Platform (GCP). Finally, you'll scale your ML models and handle heavy workloads across CPUs, GPUs, and Cloud TPUs.

By the end of this TensorFlow book, you'll have learned the patterns needed for TensorFlow Enterprise model development, data pipelines, training, and deployment.

작가정보

저자(글) KC Tung

KC Tung is a Cloud Solution Architect at Microsoft and he specializes in Machine learning, AI models development, and deployment. He has a Ph.D. in Biophysics from The University of Texas Southwestern Medical Center in Dallas and has spoken at the 2018 O'Reilly AI Conference in San Francisco and the 2019 O'Reilly Tensorflow World Conference in San Jose. He has worked on building data ingestion and feature engineering pipeline for a custom dataset in a cloud environment. He has also delivered ML models for scalable deployment.