Improve your product knowledge and ownership while building secure and scalable machine learning platforms Purchase of the print or Kindle book includes a free PDF eBook.
Key Features
- Solve large-scale machine learning challenges in the cloud with a variety of open-source and AWS tools and frameworks
- Apply risk management techniques in the machine learning lifecycle
- Understand the key challenges and risks around implementing generative AI and learn architecture patterns for some solutions
Book Description
David Ping, Head of ML Solutions Architecture at AWS, provides valuable insights and practical examples for becoming a highly skilled ML solutions architect, linking technical architecture to business-related skills. You'll start by understanding ML fundamentals and how ML can be applied to solve real-world business problems. Once you've explored a few leading problem-solving ML algorithms, this book will focus on carefully selected and updated topics like ML algorithms, including a newly added section on generative AI and large language models. You'll also learn about open-source technology such as Kubernetes/Kubeflow to build a data science environment and ML pipelines before moving on to building an enterprise ML architecture using Amazon Web Services (AWS). In this latest edition, David has updated the entire book to incorporate the latest advancements in science, technology, and solution patterns. The biggest new addition to the handbook is a comprehensive exploration of ML risk management, generative AI, and a deep understanding of the different stages of AI/ML adoption, allowing you to assess your company's position on its AI/ML journey By the end of this book, you will have gained a comprehensive understanding of AI/ML across all key aspects, including business use cases, data science, technology, real-world solutions architecture, risk management, governance, and the overall AI/ML journey. Moreover, you will possess the skills to design and construct ML solutions and platforms that effectively cater to common use cases and follow established architecture patterns, enabling you to excel as a true professional in the field.
What you will learn
- Apply ML methodologies to solve business problems
- Design a practical enterprise ML platform architecture
- Gain a deep understanding of AI risk management frameworks and techniques
- Build an end-to-end data management architecture using AWS
- Train large-scale ML models and optimize model inference latency
- Create a business application using AI services and custom models
- Dive into generative AI with use cases, architecture patterns, risks, and ethical considerations
Who this book is for
This book is for data scientists, data engineers, cloud architects, and machine learning enthusiasts who want to become machine learning solutions architects. Also, this book is a great companion for AI/ML product managers and risk officers who want to gain an understanding of ML solutions and AI risk management and AI/ML solutions architects who want to expand their scope of knowledge around AI/ML. You'll need basic knowledge of the Python programming language, AWS, linear algebra, probability, and networking concepts before you get started with this handbook.
