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Learn TensorFlow Enterprise. Build, manage, and scale machine learning workloads seamlessly using Google's TensorFlow Enterprise KC Tung

Język publikacji: angielskim
Learn TensorFlow Enterprise. Build, manage, and scale machine learning workloads seamlessly using Google's TensorFlow Enterprise KC Tung - okladka książki

Learn TensorFlow Enterprise. Build, manage, and scale machine learning workloads seamlessly using Google's TensorFlow Enterprise KC Tung - okladka książki

Autor:
KC Tung
Ocena:
Bądź pierwszym, który oceni tę książkę
Stron:
314
Dostępne formaty:
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     Mobi

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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.

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O autorze książki

KC Tung is a cloud solution architect at Microsoft and specializes in machine learning, as well as AI model 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 pipelines for custom datasets in cloud environments. He has also delivered machine learning models for scalable deployment. He is a Microsoft certified AI engineer and data engineer.

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