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Learn TensorFlow Enterprise

You're reading from Learn TensorFlow Enterprise Build, manage, and scale machine learning workloads seamlessly using Google's TensorFlow Enterprise

Product type Paperback

Published in Nov 2020

Publisher Packt

ISBN-13 9781800209145

Length 314 pages

Edition 1st Edition

Languages

Python

Tools

TensorFlow

Concepts

Artificial Intelligence

Author (1):

KC Tung

View More author details

Table of Contents (15) Chapters

Preface

1. Section 1 – TensorFlow Enterprise Services and Features

2. Chapter 1: Overview of TensorFlow Enterprise FREE CHAPTER

3. Chapter 2: Running TensorFlow Enterprise in Google AI Platform

4. Section 2 – Data Preprocessing and Modeling

5. Chapter 3: Data Preparation and Manipulation Techniques

6. Chapter 4: Reusable Models and Scalable Data Pipelines

7. Section 3 – Scaling and Tuning ML Works

8. Chapter 5: Training at Scale

9. Chapter 6: Hyperparameter Tuning

10. Section 4 – Model Optimization and Deployment

11. Chapter 7: Model Optimization

12. Chapter 8: Best Practices for Model Training and Performance

13. Chapter 9: Serving a TensorFlow Model

14. Other Books You May Enjoy

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Technical requirements

To follow along with this chapter, and for trying the example code here: https://github.com/PacktPublishing/learn-tensorflow-enterprise, you will need to clone the GitHub repository for this book, and navigate to the folder in chapter_09. You may clone the repository with the following command:

git clone https://github.com/PacktPublishing/learn-tensorflow-enterprise.git

We will work from the folder named chapter_09. Inside this folder, there is a Jupyter notebook containing source code. You will also find the flowerclassifier/001 directory, which contains a saved_model.pb file ready for your use. In the raw_images directory, you will find a few raw JPG images for testing.

The rest of the chapter is locked

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Authors (1)

Tung

Tung

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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