Design, build, and secure scalable machine learning (ML) systems to solve real-world business problems with Python and AWSPurchase of the print or Kindle book includes a free PDF eBook. Key FeaturesSolve large-scale ML challenges in the cloud with several open-source and AWS tools and frameworksApply risk management techniques in the ML life cycle and learn architecture patterns for solutionsUnderstand the challenges and risks of implementing generative AIBook DescriptionDavid Ping, Head of GenAI and ML Solution Architecture 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 life cycle 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 generative AI, the biggest new addition to the handbook is the exploration of ML risk management and a deep understanding of the different stages of AI/ML adoption.

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.

What you will learnApply ML methodologies to solve business problemsDesign a practical enterprise ML platform architectureGain an understanding of AI risk management frameworks and techniquesBuild an end-to-end data management architecture using AWSTrain large-scale ML models and optimize model inference latencyCreate a business application using AI services and custom modelsDive into generative AI with use cases, architecture patterns, and RAGWho this book is forThis book is for solutions architects working on ML projects, ML engineers transitioning to ML solution architect roles, and MLOps engineers. Additionally, data scientists and analysts who want to enhance their practical knowledge of ML systems engineering, as well as AI/ML product managers and risk officers who want to gain an understanding of ML solutions and AI risk management, will also find this book useful.

A basic knowledge of Python, AWS, linear algebra, probability, and cloud infrastructure is required before you get started with this handbook. Table of ContentsMachine Learning and Machine Learning Solutions ArchitectureBusiness Use Cases for Machine LearningMachine Learning AlgorithmsData Management for Machine LearningOpen-Source Machine Learning LibrariesKubernetes Container Orchestration InfrastructureOpen-Source ML PlatformsBuilding a Data Science Environment using AWS ML ServicesBuilding Enterprise ML Architecture with AWS ML ServicesAdvanced ML EngineeringBuilding ML solutions with AWS AI ServicesML Risk ManagementBias, Explainability, Privacy, and Adversarial AttacksProgressing Through the ML JourneyML Milestones and Research TrendsDesigning Generative AI Platform and Solutions.