The Machine Learning Solutions Architect Handbook: Create machine learning platforms to run solutions in an enterprise setting

✍ Scribed by David Ping

• Publisher: Packt Publishing
• Year: 2022
• Tongue: English
• Leaves: 440
• Category: Library

✦ Synopsis

Build highly secure and scalable machine learning platforms to support the fast-paced adoption of machine learning solutions

Key Features

• Explore different ML tools and frameworks to solve large-scale machine learning challenges in the cloud
• Build an efficient data science environment for data exploration, model building, and model training
• Learn how to implement bias detection, privacy, and explainability in ML model development

Book Description

With a highly scalable machine learning (ML) platform, organizations can quickly scale the delivery of ML products for faster business value realization, so there is a huge demand for skilled ML solutions architects in different industries. This hands-on ML book takes you through the design patterns, architectural considerations, and the latest technology that you need to know to become a successful ML solutions architect.

You'll start by understanding ML fundamentals and how ML can be applied to real-world business problems. Once you've explored some of the leading ML algorithms for solving different types of problems, the book will help you get to grips with data management and using ML libraries such as TensorFlow and PyTorch. You'll learn how to use open source technology such as Kubernetes/Kubeflow to build a data science environment and ML pipelines and then advance to building an enterprise ML architecture using Amazon Web Services (AWS) services. You'll then cover security and governance considerations, advanced ML engineering techniques, and how to apply bias detection, explainability, and privacy in ML model development. Finally, you'll get acquainted with AWS AI services and their applications in real-world use cases.

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.

What you will learn

• Apply ML methodologies to solve business problems
• Design a practical enterprise ML platform architecture
• Implement MLOps for ML workflow automation
• 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 an AI service and a custom ML model
• Use AWS services to detect data and model bias and explain models

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. Basic knowledge of the Python programming language, AWS, linear algebra, probability, and networking concepts is assumed.

Table of Contents

1. Machine Learning and Machine Learning Solutions Architecture
2. Business Use Cases for Machine Learning
3. Machine Learning Algorithms
4. Data Management for Machine Learning
5. Open Source Machine Learning Libraries
6. Kubernetes Container Orchestration Infrastructure Management
7. Open Source Machine Learning Platforms
8. Building a Data Science Environment Using AWS ML Services
9. Building an Enterprise ML Architecture with AWS ML Services
10. Advanced ML Engineering
11. ML Governance, Bias, Explainability, and Privacy
12. Building ML Solutions with AWS AI Services

✦ Table of Contents

Cover
Title Page
Copyright and Credits
Contributors
Table of Contents
Preface
Section 1: Solving Business Challenges with Machine Learning Solution Architecture
Chapter 1: Machine Learning and Machine Learning Solutions Architecture
What are AI and ML?
Supervised ML
Unsupervised ML
Reinforcement learning
ML versus traditional software
ML life cycle
Business understanding and ML problem framing
Data understanding and data preparation
Model training and evaluation
Model deployment
Model monitoring
Business metric tracking
ML challenges
ML solutions architecture
Business understanding and ML transformation
Identification and verification of ML techniques
System architecture design and implementation
ML platform workflow automation
Security and compliance
Testing your knowledge
Summary
Chapter 2: Business Use Cases for Machine Learning
ML use cases in financial services
Capital markets front office
Capital markets back office operations
Risk management and fraud
Insurance
ML use cases in media and entertainment
Content development and production
Content management and discovery
Content distribution and customer engagement
ML use cases in healthcare and life sciences
Medical imaging analysis
Drug discovery
Healthcare data management
ML use cases in manufacturing
Engineering and product design
Manufacturing operations – product quality and yield
Manufacturing operations – machine maintenance
ML use cases in retail
Product search and discovery
Target marketing
Sentiment analysis
Product demand forecasting
ML use case identification exercise
Summary
Section 2: The Science, Tools, and Infrastructure Platform for Machine Learning
Chapter 3: Machine Learning Algorithms
Technical requirements
How machines learn
Overview of ML algorithms
Consideration for choosing ML algorithms
Algorithms for classification and regression problems
Algorithms for time series analysis
Algorithms for recommendation
Algorithms for computer vision problems
Algorithms for natural language processing problems
Generative model
Hands-on exercise
Problem statement
Dataset description
Setting up a Jupyter Notebook environment
Running the exercise
Summary
Chapter 4: Data Management for Machine Learning
Technical requirements
Data management considerations for ML
Data management architecture for ML
Data storage and management
Data ingestion
Data cataloging
Data processing
Data versioning
ML feature store
Data serving for client consumption
Authentication and authorization
Data governance
Hands-on exercise – data management for ML
Creating a data lake using Lake Formation
Creating a data ingestion pipeline
Creating a Glue catalog
Discovering and querying data in the data lake
Creating an Amazon Glue ETL job to process data for ML
Building a data pipeline using Glue workflows
Summary
Chapter 5: Open Source Machine Learning Libraries
Technical requirements
Core features of open source machine learning libraries
Understanding the scikit-learn machine learning library
Installing scikit-learn
Core components of scikit-learn
Understanding the Apache Spark ML machine learning library
Installing Spark ML
Core components of the Spark ML library
Understanding the TensorFlow deep learning library
Installing Tensorflow
Core components of TensorFlow
Hands-on exercise – training a TensorFlow model
Understanding the PyTorch deep learning library
Installing PyTorch
Core components of PyTorch
Hands-on exercise – building and training a PyTorch model
Summary
Chapter 6: Kubernetes Container Orchestration Infrastructure Management
Technical requirements
Introduction to containers
Kubernetes overview and core concepts
Networking on Kubernetes
Service mesh
Security and access management
Network security
Authentication and authorization to APIs
Running ML workloads on Kubernetes
Hands-on – creating a Kubernetes infrastructure on AWS
Problem statement
Lab instruction
Summary
Section 3: Technical Architecture Design and Regulatory Considerations for Enterprise ML Platforms
Chapter 7: Open Source Machine Learning Platforms
Technical requirements
Core components of an ML platform
Open source technologies for building ML platforms
Using Kubeflow for data science environments
Building a model training environment
Registering models with a model registry
Serving models using model serving services
Automating ML pipeline workflows
Hands-on exercise – building a data science architecture using open source technologies
Part 1 – Installing Kubeflow
Part 2 – tracking experiments and models, and deploying models
Part 3 – Automating with an ML pipeline
Summary
Chapter 8: Building a Data Science Environment Using AWS ML Services
Technical requirements
Data science environment architecture using SageMaker
SageMaker Studio
SageMaker Processing
SageMaker Training Service
SageMaker Tuning
SageMaker Experiments
SageMaker Hosting
Hands-on exercise – building a data science environment using AWS services
Problem statement
Dataset
Lab instructions
Summary
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 training engine
Automation support
Model training life cycle management
Model hosting environment deep dive
Inference engine
Authentication and security control
Monitoring and logging
Adopting MLOps for ML workflows
Components of the MLOps architecture
Monitoring and logging
Hands-on exercise – building an MLOps pipeline on AWS
Creating a CloudFormation template for the ML training pipeline
Creating a CloudFormation template for the ML deployment pipeline
Summary
Chapter 10: Advanced ML Engineering
Technical requirements
Training large-scale models with distributed training
Distributed model training using data parallelism
Distributed model training using model parallelism
Achieving low latency model inference
How model inference works and opportunities for optimization
Hardware acceleration
Model optimization
Graph and operator optimization
Model compilers
Inference engine optimization
Hands-on lab – running distributed model training with PyTorch
Modifying the training script
Modifying and running the launcher notebook
Summary
Chapter 11: ML Governance, Bias, Explainability, and Privacy
Technical requirements
What is ML governance and why is it needed?
The regulatory landscape around model risk management
Common causes of ML model risks
Understanding the ML governance framework
Understanding ML bias and explainability
Bias detection and mitigation
ML explainability techniques
Designing an ML platform for governance
Data and model documentation
Model inventory
Model monitoring
Change management control
Lineage and reproducibility
Observability and auditing
Security and privacy-preserving ML
Hands-on lab – detecting bias, model explainability, and training privacy-preserving models
Overview of the scenario
Detecting bias in the training dataset
Explaining feature importance for the trained model
Training privacy-preserving models
Chapter 12: Building ML Solutions with AWS AI Services
Technical requirements
What are AI services?
Overview of AWS AI services
Amazon Comprehend
Amazon Textract
Amazon Rekognition
Amazon Transcribe
Amazon Personalize
Amazon Lex
Amazon Kendra
Evaluating AWS AI services for ML use cases
Building intelligent solutions with AI services
Automating loan document verification and data extraction
Media processing and analysis workflow
E-commerce product recommendation
Customer self-service automation with intelligent search
Designing an MLOps architecture for AI services
AWS account setup strategy for AI services and MLOps
Code promotion across environments
Monitoring operational metrics for AI services
Hands-on lab – running ML tasks using AI services
Summary
Index
About Packt
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