You're reading from The Machine Learning Solutions Architect Handbook Create machine learning platforms to run solutions in an enterprise setting

Product type Paperback

Published in Jan 2022

Publisher Packt

ISBN-13 9781801072168

Length 442 pages

Edition 1st Edition

Languages

Python

Tools

TensorFlow

Concepts

Machine Learning

Author (1):

David Ping

View More author details

Table of Contents (17) Chapters

Preface

1. Section 1: Solving Business Challenges with Machine Learning Solution Architecture

2. Chapter 1: Machine Learning and Machine Learning Solutions Architecture FREE CHAPTER

3. Chapter 2: Business Use Cases for Machine Learning

4. Section 2: The Science, Tools, and Infrastructure Platform for Machine Learning

5. Chapter 3: Machine Learning Algorithms

6. Chapter 4: Data Management for Machine Learning

7. Chapter 5: Open Source Machine Learning Libraries

8. Chapter 6: Kubernetes Container Orchestration Infrastructure Management

9. Section 3: Technical Architecture Design and Regulatory Considerations for Enterprise ML Platforms

10. Chapter 7: Open Source Machine Learning Platforms

11. Chapter 8: Building a Data Science Environment Using AWS ML Services

12. Chapter 9: Building an Enterprise ML Architecture with AWS ML Services

13. Chapter 10: Advanced ML Engineering

14. Chapter 11: ML Governance, Bias, Explainability, and Privacy

15. Chapter 12: Building ML Solutions with AWS AI Services

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PyTorch is an open source machine learning library that was designed for deep learning using GPUs and CPUs. It was released in 2016, and it is a highly popular machine learning framework with a large following and many adoptions. Many technology companies, including tech giants such as Facebook, Microsoft, and Airbnb, all use PyTorch heavily for a wide range of deep learning use cases, such as computer vision and natural language processing.

PyTorch strikes a good balance of performance (using a C++ backend) with ease of use with default support for dynamic computational graphs and interoperability with the rest of the Python ecosystem. For example, with PyTorch, you can easily convert between NumPy arrays and PyTorch tensors. To allow for easy backward propagation, PyTorch has built-in support for automatically computing gradients, a vital requirement for gradient-based model optimization.

The PyTorch library consists of several...