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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

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Product type Paperback
Published in Nov 2020
Publisher Packt
ISBN-13 9781800209145
Length 314 pages
Edition 1st Edition
Languages
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Author (1):
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KC Tung KC Tung
Author Profile Icon KC Tung
KC Tung
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Toc

Table of Contents (15) Chapters Close

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

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.

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