Book Image

The Machine Learning Solutions Architect Handbook

By : David Ping
Book Image

The Machine Learning Solutions Architect Handbook

By: David Ping

Overview of this book

When equipped with a highly scalable machine learning (ML) platform, organizations can quickly scale the delivery of ML products for faster business value realization. There is a huge demand for skilled ML solutions architects in different industries, and this handbook will help you master the design patterns, architectural considerations, and the latest technology insights you’ll need to become one. 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 help you tackle data management and get the most out of ML libraries such as TensorFlow and PyTorch. Using open source technology such as Kubernetes/Kubeflow to build a data science environment and ML pipelines will be covered next, before moving on to building an enterprise ML architecture using Amazon Web Services (AWS). You’ll also learn about security and governance considerations, advanced ML engineering techniques, and how to apply bias detection, explainability, and privacy in ML model development. By the end of this book, you’ll be able to design and build an ML platform to support common use cases and architecture patterns like a true professional.

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Table of Contents (17 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Conventions used
Get in touch
Share Your Thoughts
1
Section 1: Solving Business Challenges with Machine Learning Solution Architecture
Section 1: Solving Business Challenges with Machine Learning Solution Architecture
Free Chapter
2
Chapter 1: Machine Learning and Machine Learning Solutions Architecture
Chapter 1: Machine Learning and Machine Learning Solutions Architecture
What are AI and ML?
ML versus traditional software
ML life cycle
ML challenges
ML solutions architecture
Testing your knowledge
Summary
3
Chapter 2: Business Use Cases for Machine Learning
Chapter 2: Business Use Cases for Machine Learning
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 case identification exercise
Summary
4
Section 2: The Science, Tools, and Infrastructure Platform for Machine Learning
Section 2: The Science, Tools, and Infrastructure Platform for Machine Learning
5
Chapter 3: Machine Learning Algorithms
Chapter 3: Machine Learning Algorithms
Technical requirements
How machines learn
Overview of ML algorithms
Hands-on exercise
Summary
6
Chapter 4: Data Management for Machine Learning
Chapter 4: Data Management for Machine Learning
Technical requirements
Data management considerations for ML
Data management architecture for ML
Hands-on exercise – data management for ML
Summary
7
Chapter 5: Open Source Machine Learning Libraries
Chapter 5: Open Source Machine Learning Libraries
Technical requirements
Core features of open source machine learning libraries
Understanding the scikit-learn machine learning library
Understanding the Apache Spark ML machine learning library
Understanding the TensorFlow deep learning library
Hands-on exercise – training a TensorFlow model
Understanding the PyTorch deep learning library
Hands-on exercise – building and training a PyTorch model
Summary
8
Chapter 6: Kubernetes Container Orchestration Infrastructure Management
Chapter 6: Kubernetes Container Orchestration Infrastructure Management
Technical requirements
Introduction to containers
Kubernetes overview and core concepts
Networking on Kubernetes
Security and access management
Hands-on – creating a Kubernetes infrastructure on AWS
Summary
9
Section 3: Technical Architecture Design and Regulatory Considerations for Enterprise ML Platforms
Section 3: Technical Architecture Design and Regulatory Considerations for Enterprise ML Platforms
10
Chapter 7: Open Source Machine Learning Platforms
Chapter 7: Open Source Machine Learning Platforms
Technical requirements
Core components of an ML platform
Open source technologies for building ML platforms
Hands-on exercise – building a data science architecture using open source technologies
Summary
11
Chapter 8: Building a Data Science Environment Using AWS ML Services
Chapter 8: Building a Data Science Environment Using AWS ML Services
Technical requirements
Data science environment architecture using SageMaker
Hands-on exercise – building a data science environment using AWS services
Summary
12
Chapter 9: Building an Enterprise ML Architecture with AWS ML Services
Chapter 9: Building an Enterprise ML Architecture with AWS ML Services
Technical requirements
Key requirements for an enterprise ML platform
Enterprise ML architecture pattern overview
Model training environment
Model hosting environment deep dive
Adopting MLOps for ML workflows
Hands-on exercise – building an MLOps pipeline on AWS
Summary
13
Chapter 10: Advanced ML Engineering
Chapter 10: 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
14
Chapter 11: ML Governance, Bias, Explainability, and Privacy
Chapter 11: ML Governance, Bias, Explainability, and Privacy
Technical requirements
What is ML governance and why is it needed?
Understanding the ML governance framework
Understanding ML bias and explainability
Designing an ML platform for governance
Hands-on lab – detecting bias, model explainability, and training privacy-preserving models
15
Chapter 12: Building ML Solutions with AWS AI Services
Chapter 12: 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
Summary
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16
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Chapter 1: Machine Learning and Machine Learning Solutions Architecture

The field of artificial intelligence (AI) and machine learning (ML) has had a long history. Over the last 70+ years, ML has evolved from checker game-playing computer programs in the 1950s to advanced AI capable of beating the human world champion in the game of Go. Along the way, the technology infrastructure for ML has also evolved from a single machine/server for small experiments and models to highly complex end-to-end ML platforms capable of training, managing, and deploying tens of thousands of ML models. The hyper-growth in the AI/ML field has resulted in the creation of many new professional roles, such as MLOps engineering, ML product management, and ML software engineering across a range of industries.

Machine learning solutions architecture (ML solutions architecture) is another relatively new discipline that is playing an increasingly critical role in the full end-to-end ML life cycle as ML projects become increasingly complex in terms of business impact, science sophistication, and the technology landscape.

This chapter talks about the basic concepts of ML and where ML solutions architecture fits in the full data science life cycle. You will learn the three main types of ML, including supervised, unsupervised, and reinforcement learning. We will discuss the different steps it will take to get an ML project from the ideas stage to production and the challenges faced by organizations when implementing an ML initiative. Finally, we will finish the chapter by briefly discussing the core focus areas of ML solutions architecture, including system architecture, workflow automation, and security and compliance.

Upon completing this chapter, you should be able to identify the three main ML types and what type of problems they are designed to solve. You will understand the role of an ML solutions architect and what business and technology areas you need to focus on to support end-to-end ML initiatives.

In this chapter, we are going to cover the following main topics:

  • What is ML, and how does it work?
  • The ML life cycle and its key challenges
  • What is ML solutions architecture, and where does it fit in the overall life cycle?
Previous Section
End of Section 1
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