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

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
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