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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 2. Exploring ML Business Use Cases FREE CHAPTER 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

ML use cases in automotive

The automotive industry has undergone significant transformation in recent years, with technology playing a key role in shaping its evolution. AI and ML have emerged as powerful tools for automakers and suppliers to improve efficiency, safety, and customer experience. From production lines to connected cars, AI and ML are being used to automate processes, optimize operations, and enable new services and features.

Autonomous vehicle

One of the most significant applications of AI and ML in the automotive industry is in autonomous driving. Automakers and tech companies are leveraging these technologies to build self-driving vehicles that can safely navigate roads and highways without human intervention. AI and ML algorithms are used to process data from sensors, cameras, and other inputs to make real-time decisions and actions, such as braking or changing lanes. The system architecture of an autonomous vehicle (AV) consists of 3 main stages: 1/ perception and...

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