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The Machine Learning Solutions Architect Handbook

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

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Product type Paperback
Published in Jan 2022
Publisher Packt
ISBN-13 9781801072168
Length 442 pages
Edition 1st Edition
Languages
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Author (1):
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David Ping David Ping
Author Profile Icon David Ping
David Ping
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Toc

Table of Contents (17) Chapters Close

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

Chapter 12: Building ML Solutions with AWS AI Services

You have come a long way and we are getting close to the finishing line. Up to this point, we have mainly focused on the skills and technologies required to build and deploy ML models using open source technologies and managed ML platforms. To solve business problems with machine learning, however, you don't always have to build, train, and deploy your ML models from scratch. An alternative option is to use fully managed AI services. AI services are fully managed APIs or applications with pre-trained models that perform specific ML tasks, such as object detection or sentiment analysis. Some AI services also allow you to train custom models with your data for a defined ML task, such as document classification. AI services promise to enable organizations to build ML-enabled solutions without requiring strong ML competencies.

In this final chapter, we are going to switch gears and talk about several AWS AI services and where...

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