The widespread of solutions powered by generative AI and large language models has led to a surge in the demand for machine learning engineers capable of building, managing, and scaling complex Gen AI-powered applications and systems.
This book starts by introducing relevant concepts such as Machine Learning Engineering, Generative AI, Large Language Models (LLMs), and MLOps. As you progress through each of the chapters of the book, you will learn how to leverage these concepts as well as various AWS services and solutions to build, manage, and optimize machine learning systems. In addition to this, you'll discover how to automate the LLMOps pipeline, optimize deep learning experiments, and make use of proven deployment strategies when dealing with LLMs. You'll also learn how to create Gen AI applications powered by retrieval-augmented generation (RAG). To help expand your knowledge and elevate your expertise on machine learning engineering, each chapter includes practical examples and clear explanations to help you manage, troubleshoot, and optimize Generative AI systems running on AWS.
By the end of this book, you'll be able to operationalize and secure Generative AI applications on AWS, which will give you the experience and confidence needed for solving a wide variety of ML engineering challenges and requirements.

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Spis treści

Machine Learning Engineering on AWS. Operationalize and optimize Generative AI systems and LLMOps pipelines in production - Second Edition eBook -- spis treści

• 1. A Gentle Introduction to Generative AI on AWS
• 2. Exploring the High-Level AI/ML services of AWS
• 3. Machine Learning Engineering with Amazon SageMaker
• 4. Practical Data Management on AWS
• 5. Pragmatic Data Processing and Analysis
• 6. Getting Started with SageMaker Training Solutions
• 7. Diving Deeper into SageMaker Training Solutions
• 8. Model Evaluation, Benchmarking, and Bias Detection
• 9. Machine Learning Model Deployment on AWS
• 10. Machine Learning Model Deployment Strategies
• 11. Model Monitoring & Management Solutions
• 12. Security, Governance, and Compliance Strategies
• 13. Machine Learning Pipelines with SageMaker Pipelines Part I
• 14. Machine Learning Pipelines with SageMaker Pipelines Part II