Book description
Design, build, and secure scalable machine learning (ML) systems to solve real-world business problems with Python and AWS Purchase of the print or Kindle book includes a free PDF eBook
Key Features
- Go in-depth into the ML lifecycle, from ideation and data management to deployment and scaling
- Apply risk management techniques in the ML lifecycle and design architectural patterns for various ML platforms and solutions
- Understand the generative AI lifecycle, its core technologies, and implementation risks
Book Description
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.
What you will learn
- Apply ML methodologies to solve business problems across industries
- Design a practical enterprise ML platform architecture
- Gain an 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 artificial intelligence services and custom models
- Dive into generative AI with use cases, architecture patterns, and RAG
Who this book is for
This 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 contents
- Preface
- Navigating the ML Lifecycle with ML Solutions Architecture
-
Exploring ML Business Use Cases
- ML use cases in financial services
- ML use cases in media and entertainment
- ML use cases in healthcare and life sciences
- ML use cases in manufacturing
- ML use cases in retail
- ML use cases in the automotive industry
- Summary
-
Exploring ML Algorithms
- Technical requirements
- How machines learn
-
Overview of ML algorithms
- Consideration for choosing ML algorithms
- Algorithms for classification and regression problems
- Algorithms for clustering
- Algorithms for time series analysis
- Algorithms for recommendation
- Algorithms for computer vision problems
- Algorithms for natural language processing (NLP) problems
- Generative AI algorithms
- Hands-on exercise
- Summary
-
Data Management for ML
- Technical requirements
- Data management considerations for ML
- Data management architecture for ML
- Hands-on exercise – data management for ML
- Summary
-
Exploring Open-Source ML Libraries
- Technical requirements
- Core features of open-source ML libraries
- Understanding the scikit-learn ML library
- Understanding the Apache Spark ML library
- Understanding the TensorFlow deep learning library
- Understanding the PyTorch deep learning library
- How to choose between TensorFlow and PyTorch
- Summary
- Kubernetes Container Orchestration Infrastructure Management
-
Open-Source ML Platforms
- Core components of an ML platform
- Open-source technologies for building ML platforms
- Designing an end-to-end ML platform
- Summary
-
Building a Data Science Environment Using AWS ML Services
- Technical requirements
- SageMaker overview
-
Data science environment architecture using SageMaker
- Onboarding SageMaker users
- Launching Studio applications
- Preparing data
- Preparing data interactively with SageMaker Data Wrangler
- Preparing data at scale interactively
- Processing data as separate jobs
- Creating, storing, and sharing features
- Training ML models
- Tuning ML models
- Deploying ML models for testing
- Best practices for building a data science environment
- Hands-on exercise – building a data science environment using AWS services
- Summary
- Designing an Enterprise ML Architecture with AWS ML Services
-
Advanced ML Engineering
- Technical requirements
- Training large-scale models with distributed training
- Achieving low-latency model inference
- Hands-on lab – running distributed model training with PyTorch
- Summary
- Building ML Solutions with AWS AI Services
- AI Risk Management
- Bias, Explainability, Privacy, and Adversarial Attacks
- Charting the Course of Your ML Journey
-
Navigating the Generative AI Project Lifecycle
- The advancement and economic impact of generative AI
- What industries are doing with generative AI
- The lifecycle of a generative AI project and the core technologies
- The limitations, risks, and challenges of adopting generative AI
- Summary
-
Designing Generative AI Platforms and Solutions
- Operational considerations for generative AI platforms and solutions
- The retrieval-augmented generation pattern
- Choosing an LLM adaptation method
- Bringing it all together
- Considerations for deploying generative AI applications in production
- Practical generative AI business solutions
- Are we close to having artificial general intelligence?
- Summary
- Other Books You May Enjoy
- Index
Product information
- Title: The Machine Learning Solutions Architect Handbook - Second Edition
- Author(s):
- Release date: April 2024
- Publisher(s): Packt Publishing
- ISBN: 9781805122500
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