David Ping, Head of GenAI and ML Solution Architecture for global industries at AWS, provides expert insights and practical examples to help you become a proficient ML solutions architect, linking technical architecture to business-related skills.
You'll learn about ML algorithms, cloud infrastructure, system design, MLOps , and how to apply ML to solve real-world business problems. David explains the generative AI project lifecycle and examines Retrieval Augmented Generation (RAG), an effective architecture pattern for generative AI applications. You’ll also learn about open-source technologies, such as Kubernetes/Kubeflow, for building a data science environment and ML pipelines before building an enterprise ML architecture using AWS. As well as ML risk management and the different stages of AI/ML adoption, the biggest new addition to the handbook is the deep exploration of generative AI.
By the end of this book , you’ll have gained a comprehensive understanding of AI/ML across all key aspects, including business use cases, data science, real-world solution architecture, risk management, and governance. You’ll possess the skills to design and construct ML solutions that effectively cater to common use cases and follow established ML architecture patterns, enabling you to excel as a true professional in the field.

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Spis treści

The Machine Learning Solutions Architect Handbook. Practical strategies and best practices on the ML lifecycle, system design, MLOps, and generative AI - Second Edition eBook -- spis treści

• 1. Navigating the ML Lifecycle with ML Solutions Architecture
• 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