Machine Learning Engineering on AWS: Build, deploy, and operationalize LLMs, AI agents, and generative AI systems on AWS - Softcover
Synopsis
Solve machine learning engineering challenges for GenAI-powered systems and AI agents on AWS, and automate LLMOps pipelines using Amazon Bedrock, SageMaker AI, Bedrock AgentCore, and Strands Agents.
Free with your book: DRM-free PDF version + access to Packt's next-gen Reader*
Key Features
- Build and scale AI agents using Amazon Bedrock AgentCore and Strands Agents
- Fine-tune, evaluate, and deploy ML models using Amazon SageMaker AI
- Automate LLMOps workflows with SageMaker Pipelines
Book Description
Modern AI systems increasingly leverage large language models, retrieval-augmented generation, and AI agents to power generative AI applications in the cloud. As organizations operationalize these systems at scale, there is a growing need for engineers with strong machine learning engineering expertise. To stay ahead in this rapidly evolving field, you need a deep understanding of AI and ML concepts as well as, practical, hands-on experience with the platforms and tools used to build and operate production-grade AI systems.
Machine Learning Engineering on AWS is a practical guide that shows you how to use AWS services such as Amazon Bedrock and Amazon SageMaker AI to fine-tune, evaluate, and deploy LLMs and generative AI systems. You'll learn how to develop RAG-powered systems, build and deploy AI agents using Bedrock AgentCore and Strands Agents, evaluate models using LLM-as-a-judge techniques, and automate LLMOps pipelines using SageMaker Pipelines. The book also covers best practices for building scalable, secure, and production-ready GenAI systems.
AWS AI hero Joshua Arvin Lat equips you with the skills and practical knowledge to handle a wide variety of ML engineering requirements, helping you design, operationalize, and secure generative AI systems and AI agents on AWS with confidence.
*Email sign-up and proof of purchase required"
What you will learn
- Build and deploy AI agents using Bedrock AgentCore and Strands Agents
- Dive deep into ML engineering with Amazon SageMaker AI
- Evaluate model performance using LLM-as-a-judge
- Explore advanced model fine-tuning and deployment using SageMaker AI
- Build RAG-powered systems using Bedrock Knowledge Bases and S3 Vectors
- Modernize analytics with a managed transactional data lake
- Automate LLMOps pipelines using SageMaker Pipelines and AWS Lambda
- Explore best practices for building GenAI systems and AI agents on AWS
Who this book is for
This book is intended for AI engineers, data scientists, machine learning engineers, and technology leaders who want to deepen their understanding of machine learning engineering, generative AI, large language models, retrieval-augmented generation, AI agents, and MLOps on AWS. A foundational understanding of artificial intelligence, machine learning, generative AI, and cloud engineering concepts is recommended.
Table of Contents
- A Gentle Introduction to Generative AI and AI Agents on AWS
- Building AI Agents with SageMaker AI and Bedrock AgentCore
- Machine Learning Engineering with Amazon SageMaker AI
- Modernizing Analytics with a Managed Transactional Data Lake
- Practical Data Management on AWS
- Pragmatic Data Processing on AWS
- SageMaker AI Model Training and Tuning Capabilities
- SageMaker AI Model Deployment Options and Strategies
- Automating LLMOps Workflows with SageMaker Pipelines
"synopsis" may belong to another edition of this title.
About the Author
Joshua Arvin Lat serves as the Vice President of Cybersecurity and AI for Axos. He previously held Chief Technology Officer and Director roles across SaaS platforms, AI automation companies, e-commerce startups, and digital agencies. Because of his proven track record in leading digital transformation within organizations, he has been recognized as one of the winners of the prestigious Orange Boomerang: Digital Leader of the Year 2023 award. Years ago, he led a team that won first place in a global cybersecurity competition for their published research. He is also an AWS AI Hero and has spoken at several international conferences on practical applications of generative AI, software engineering, cybersecurity, and management. To give back to the technology community, he has authored five books covering artificial intelligence, cloud computing, and cybersecurity.
"About this title" may belong to another edition of this title.
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Machine Learning Engineering on AWS: Build, deploy, and operationalize LLMs, AI agents, and generative AI systems on AWS
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Machine Learning Engineering on AWS (Paperback)
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ISBN 13: 9781835881088
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Paperback. Condition: new. Paperback. Solve machine learning engineering challenges for GenAI-powered systems and AI agents on AWS, and automate LLMOps pipelines using Amazon Bedrock, SageMaker AI, Bedrock AgentCore, and Strands Agents.Free with your book: DRM-free PDF version + access to Packt's next-gen Reader*Key FeaturesBuild and scale AI agents using Amazon Bedrock AgentCore and Strands AgentsFine-tune, evaluate, and deploy ML models using Amazon SageMaker AIAutomate LLMOps workflows with SageMaker PipelinesBook DescriptionModern AI systems increasingly leverage large language models, retrieval-augmented generation, and AI agents to power generative AI applications in the cloud. As organizations operationalize these systems at scale, there is a growing need for engineers with strong machine learning engineering expertise. To stay ahead in this rapidly evolving field, you need a deep understanding of AI and ML concepts as well as, practical, hands-on experience with the platforms and tools used to build and operate production-grade AI systems.Machine Learning Engineering on AWS is a practical guide that shows you how to use AWS services such as Amazon Bedrock and Amazon SageMaker AI to fine-tune, evaluate, and deploy LLMs and generative AI systems. You'll learn how to develop RAG-powered systems, build and deploy AI agents using Bedrock AgentCore and Strands Agents, evaluate models using LLM-as-a-judge techniques, and automate LLMOps pipelines using SageMaker Pipelines. The book also covers best practices for building scalable, secure, and production-ready GenAI systems.AWS AI hero Joshua Arvin Lat equips you with the skills and practical knowledge to handle a wide variety of ML engineering requirements, helping you design, operationalize, and secure generative AI systems and AI agents on AWS with confidence.*Email sign-up and proof of purchase required"What you will learnBuild and deploy AI agents using Bedrock AgentCore and Strands AgentsDive deep into ML engineering with Amazon SageMaker AIEvaluate model performance using LLM-as-a-judgeExplore advanced model fine-tuning and deployment using SageMaker AIBuild RAG-powered systems using Bedrock Knowledge Bases and S3 VectorsModernize analytics with a managed transactional data lakeAutomate LLMOps pipelines using SageMaker Pipelines and AWS LambdaExplore best practices for building GenAI systems and AI agents on AWSWho this book is forThis book is intended for AI engineers, data scientists, machine learning engineers, and technology leaders who want to deepen their understanding of machine learning engineering, generative AI, large language models, retrieval-augmented generation, AI agents, and MLOps on AWS. A foundational understanding of artificial intelligence, machine learning, generative AI, and cloud engineering concepts is recommended. This hands-on book teaches you how to design, build, optimize, and secure generative AI systems and AI agents on AWS. As you progress through the chapters, you'll leverage various AWS services to automate end-to-end LLMOps pipelines. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9781835881088
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Machine Learning Engineering on AWS (Paperback)
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ISBN 13: 9781835881088
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Paperback. Condition: new. Paperback. Solve machine learning engineering challenges for GenAI-powered systems and AI agents on AWS, and automate LLMOps pipelines using Amazon Bedrock, SageMaker AI, Bedrock AgentCore, and Strands Agents.Free with your book: DRM-free PDF version + access to Packt's next-gen Reader*Key FeaturesBuild and scale AI agents using Amazon Bedrock AgentCore and Strands AgentsFine-tune, evaluate, and deploy ML models using Amazon SageMaker AIAutomate LLMOps workflows with SageMaker PipelinesBook DescriptionModern AI systems increasingly leverage large language models, retrieval-augmented generation, and AI agents to power generative AI applications in the cloud. As organizations operationalize these systems at scale, there is a growing need for engineers with strong machine learning engineering expertise. To stay ahead in this rapidly evolving field, you need a deep understanding of AI and ML concepts as well as, practical, hands-on experience with the platforms and tools used to build and operate production-grade AI systems.Machine Learning Engineering on AWS is a practical guide that shows you how to use AWS services such as Amazon Bedrock and Amazon SageMaker AI to fine-tune, evaluate, and deploy LLMs and generative AI systems. You'll learn how to develop RAG-powered systems, build and deploy AI agents using Bedrock AgentCore and Strands Agents, evaluate models using LLM-as-a-judge techniques, and automate LLMOps pipelines using SageMaker Pipelines. The book also covers best practices for building scalable, secure, and production-ready GenAI systems.AWS AI hero Joshua Arvin Lat equips you with the skills and practical knowledge to handle a wide variety of ML engineering requirements, helping you design, operationalize, and secure generative AI systems and AI agents on AWS with confidence.*Email sign-up and proof of purchase required"What you will learnBuild and deploy AI agents using Bedrock AgentCore and Strands AgentsDive deep into ML engineering with Amazon SageMaker AIEvaluate model performance using LLM-as-a-judgeExplore advanced model fine-tuning and deployment using SageMaker AIBuild RAG-powered systems using Bedrock Knowledge Bases and S3 VectorsModernize analytics with a managed transactional data lakeAutomate LLMOps pipelines using SageMaker Pipelines and AWS LambdaExplore best practices for building GenAI systems and AI agents on AWSWho this book is forThis book is intended for AI engineers, data scientists, machine learning engineers, and technology leaders who want to deepen their understanding of machine learning engineering, generative AI, large language models, retrieval-augmented generation, AI agents, and MLOps on AWS. A foundational understanding of artificial intelligence, machine learning, generative AI, and cloud engineering concepts is recommended. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9781835881088
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