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The Machine Learning Solutions Architect Handbook

You're reading from   The Machine Learning Solutions Architect Handbook Practical strategies and best practices on the ML lifecycle, system design, MLOps, and generative AI

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Product type Paperback
Published in Apr 2024
Publisher Packt
ISBN-13 9781805122500
Length 602 pages
Edition 2nd Edition
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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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Table of Contents (19) Chapters Close

Preface 1. Navigating the ML Lifecycle with ML Solutions Architecture FREE CHAPTER 2. Exploring ML Business Use Cases 3. Exploring ML Algorithms 4. Data Management for ML 5. Exploring Open-Source ML Libraries 6. Kubernetes Container Orchestration Infrastructure Management 7. Open-Source ML Platforms 8. Building a Data Science Environment Using AWS ML Services 9. Designing an Enterprise ML Architecture with AWS ML Services 10. Advanced ML Engineering 11. Building ML Solutions with AWS AI Services 12. AI Risk Management 13. Bias, Explainability, Privacy, and Adversarial Attacks 14. Charting the Course of Your ML Journey 15. Navigating the Generative AI Project Lifecycle 16. Designing Generative AI Platforms and Solutions 17. Other Books You May Enjoy
18. Index

Understanding AI risk scenarios

Many of the organizations I have worked with have very limited knowledge about the risks presented in their AI systems. They often treat AI risks the same way they deal with risks associated with traditional software. In reality, AI systems present a new set of risks that we do not normally see in traditional software. With traditional software, the risk is mainly about software vulnerability, a legacy technology stack, malware, misconfiguration, and unauthorized access to data. AI systems are exposed to many of the same software risks; additionally, AI systems can present new kinds of risks such as bias and misinformation. These risks can have significant negative consequences for organizations and individuals that rely on AI systems for business operations and decision-making. AI risks can manifest in many different ways, such as displaying biased behavior or producing unexpected prediction results. Many of the AI risk scenarios are also silent risks...

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