Book Image

The Machine Learning Solutions Architect Handbook - Second Edition

By : David Ping
5 (1)
Book Image

The Machine Learning Solutions Architect Handbook - Second Edition

5 (1)
By: David Ping

Overview of this book

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.

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Table of Contents (19 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Navigating the ML Lifecycle with ML Solutions Architecture
Navigating the ML Lifecycle with ML Solutions Architecture
ML versus traditional software
ML lifecycle
ML challenges
ML solutions architecture
Summary
2
Exploring ML Business Use Cases
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
3
Exploring ML Algorithms
Exploring ML Algorithms
Technical requirements
How machines learn
Overview of ML algorithms
Hands-on exercise
Summary
4
Data Management for ML
Data Management for ML
Technical requirements
Data management considerations for ML
Data management architecture for ML
Hands-on exercise – data management for ML
Summary
5
Exploring Open-Source ML Libraries
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
6
Kubernetes Container Orchestration Infrastructure Management
Kubernetes Container Orchestration Infrastructure Management
Technical requirements
Introduction to containers
Overview of Kubernetes and its core concepts
Networking on Kubernetes
Security and access management
Hands-on – creating a Kubernetes infrastructure on AWS
Summary
7
Open-Source ML Platforms
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
8
Building a Data Science Environment Using AWS ML Services
Building a Data Science Environment Using AWS ML Services
Technical requirements
SageMaker overview
Data science environment architecture using SageMaker
Best practices for building a data science environment
Hands-on exercise – building a data science environment using AWS services
Summary
9
Designing an Enterprise ML Architecture with AWS ML Services
Designing an Enterprise ML Architecture with AWS ML Services
Technical requirements
Key considerations for ML platforms
Key requirements for an enterprise ML platform
Enterprise ML architecture pattern overview
Adopting MLOps for ML workflows
Best practices in building and operating an ML platform
Summary
10
Advanced ML Engineering
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
11
Building ML Solutions with AWS AI Services
Building ML Solutions with AWS AI Services
Technical requirements
What are AI services?
Overview of AWS AI services
Building intelligent solutions with AI services
Designing an MLOps architecture for AI services
Hands-on lab – running ML tasks using AI services
12
AI Risk Management
AI Risk Management
Understanding AI risk scenarios
The regulatory landscape around AI risk management
Understanding AI risk management
Applying risk management across the AI lifecycle
Designing ML platforms with governance and risk management considerations
Summary
13
Bias, Explainability, Privacy, and Adversarial Attacks
Bias, Explainability, Privacy, and Adversarial Attacks
Understanding bias
Understanding ML explainability
Understanding security and privacy-preserving ML
Understanding adversarial attacks
Hands-on lab – detecting bias, explaining models, training privacy-preserving mode, and simulating adversarial attack
Summary
14
Charting the Course of Your ML Journey
Charting the Course of Your ML Journey
ML adoption stages
AI/ML maturity and assessment
AI/ML operating models
Solving ML journey challenges
Summary
15
Navigating the Generative AI Project Lifecycle
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
16
Designing Generative AI Platforms and Solutions
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
17
Other Books You May Enjoy
Other Books You May Enjoy
18
Index
Index

ML challenges

Over the years, I have worked on many real-world problems using ML solutions and encountered different challenges faced by different industries during ML adoptions.

I often get the same question when working on ML projects: We have a lot of data – can you help us figure out what insights we can generate using ML? I refer to companies with this question as having a business use case challenge. Not being able to identify business use cases for ML is a very big hurdle for many companies. Without a properly identified business problem and its value proposition and benefit, it becomes difficult to initiate an ML project.

In my conversations with different companies across their industries, data-related challenges emerge as a frequent issue. This includes data quality, data inventory, data accessibility, data governance, and data availability. This problem affects both data-poor and data-rich companies and is often exacerbated by data silos, data security, and industry regulations.

The shortage of data science and ML talent is another major challenge I have heard from many companies. Companies, in general, are having a tough time attracting and retaining top ML talents, which is a common problem across all industries. As ML platforms become more complex and the scope of ML projects increases, the need for other ML-related functions starts to surface. Nowadays, in addition to just data scientists, an organization would also need functional roles for ML product management, ML infrastructure engineering, and ML operations management.

Based on my experiences, I have observed that cultural acceptance of ML-based solutions is another significant challenge for broad adoption. There are individuals who perceive ML as a threat to their job functions, and their lack of knowledge in ML makes them hesitant to adopt these new methods in their business workflows.

The practice of ML solutions architecture aims to help solve some of the challenges in ML. In the next section, we will explore ML solutions architecture and its role in the ML lifecycle.

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