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

16. Other Books You May Enjoy

ML use cases in manufacturing

Manufacturing is an industry sector that produces tangible finished products. It includes many sub-sectors such as consumer goods, electronics goods, industrial equipment, automobiles, furniture, building materials, sporting goods, clothing, and toys. There are multiple stages in a typical product manufacturing life cycle, including product design, prototyping, manufacturing and assembling, and post-manufacturing service and support. The following diagram shows the typical business functions and flow in the manufacturing sector:

Figure 2.16 – Manufacturing business process flow

AI and ML have played an essential role in the manufacturing process, such as sales forecasting, predictive machine maintenance, quality control and robotic automation for manufacturing quality and yield, and process and supply chain optimization to improve overall operational efficiency.