Learn TensorFlow Enterprise: Build,manage,and scale machine learning workloads seamlessly using Google's TensorFlow Enterprise
by: KC Tung
Publisher Finelybook 出版社： Packt Publishing (November 27,2020)
Language 语言： English
pages 页数： 314 pages
ISBN-10 书号： 1800209142
ISBN-13 书号： 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
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