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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

Working with TensorFlow Estimators

TensorFlow estimators are also reusable components. The Estimators are higher-level APIs that enable users to build, train, and deploy machine learning models. It has several pre-made models that can save users from the hassle of creating computational graphs or sessions. This makes it easier for users to try different model architectures quickly with limited code changes. The Estimators are not specifically dedicated to deep learning models in the same way as tf.keras. Therefore, you will not find a lot of pre-made deep learning models available. If you need to work with deep learning frameworks, then the tf.keras API is the right choice to get started.

For this example, we are going to set up the same regression problem and build a regression model. The source of data is the same one we used in streaming training data, which is available through Google Cloud's BigQuery:

DATASET_GCP_PROJECT_ID = 'bigquery-public-data'
DATASET_ID...
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