Learn TensorFlow Enterprise: Build, manage, and scale machine learning workloads seamlessly using Google's TensorFlow Enterprise
Language: English
Published by Packt Publishing (edition ), 2020
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- Title
- Learn TensorFlow Enterprise: Build, manage, and scale machine learning workloads seamlessly using Google's TensorFlow Enterprise
- Author
- KC Tung
- Publisher
- Packt Publishing (edition )
- Publication year
- 2020
- Condition
- Very Good
- Binding
- Paperback
- Language
- English
- ISBN 10
- 1800209142
- ISBN 13
- 9781800209145
Use TensorFlow Enterprise with other GCP services to improve the speed and efficiency of machine learning pipelines for reliable and stable enterprise-level deployment
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
Book Description
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.
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
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.
Table of Contents
- Overview of TensorFlow Enterprise
- Running TensorFlow Enterprise in Google AI Platform
- Data Preparation and Manipulation Techniques
- Reusable Models and Scalable Data Pipelines
- Training at Scale
- Hyperparameter Tuning
- Model Optimization
- Best Practices for Model Training and Performance
- Serving a TensorFlow Model
"Synopsis" may belong to another edition of this title.
About the Author
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.
"About the title" may belong to another edition of this title.
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