Engineering MLOps: Rapidly build, test, and manage production-ready machine learning life cycles at scale

Cover
Title Page
Copyright and Credits
Contributors
Table of Contents
Preface
Section 1: Framework for Building Machine Learning Models
Chapter 1: Fundamentals of an MLOps Workflow
	The evolution of infrastructure and software development
		The rise of machine learning and deep learning
		The end of Moore\'s law
		AI-centric applications
		Software development evolution
	Traditional software development challenges
	Trends of ML adoption in software development
	Understanding MLOps
	Concepts and workflow of MLOps
		Discussing a use case
	Summary
Chapter 2: Characterizing Your Machine Learning Problem
	The ML solution development process
	Types of ML models
		Learning models
		Hybrid models
		Statistical models
		HITL models
	Structuring your MLOps
		Small data ops
		Big data ops
		Hybrid MLOps
		Large-scale MLOps
	An implementation roadmap for your solution
		Phase 1 – ML development
		Phase 2 – Transition to operations
		Phase 3 – Operations
	Procuring data, requirements, and tools
		Data
		Requirements
	Tools and infrastructure
	Discussing a real-life business problem
	Summary
Chapter 3: Code Meets Data
	Business problem analysis and categorizing the problem
	Setting up the resources and tools
		Installing MLflow
		Azure Machine Learning
		Azure DevOps
		JupyterHub
	10 principles of source code management for ML
	What is good data for ML?
	Data preprocessing
		Data quality assessment
		Calibrating missing data
		Label encoding 
		New feature – Future_weather_condition
		Data correlations and filtering
		Time series analysis
	Data registration and versioning
	Toward the ML Pipeline
		Feature Store
	Summary
Chapter 4: Machine Learning Pipelines
	Going through the basics of ML pipelines
	Data ingestion and feature engineering
		Data ingestion (training dataset)
	Machine learning training and hyperparameter optimization
		Support Vector Machine
		Random Forest classifier
	Model testing and defining metrics
		Testing the SVM classifier
		Testing the Random Forest classifier
	Model packaging
	Registering models and production artifacts
		Registering production artifacts
	Summary
Chapter 5: Model Evaluation and Packaging
	Model evaluation and interpretability metrics
		Learning models\' metrics
		Hybrid models\' metrics
		Statistical models\' metrics
		HITL model metrics
	Production testing methods
		Batch testing
		A/B testing
		Stage test or shadow test
		Testing in CI/CD
	Why package ML models?
		Portability
		Inference
		Interoperability
		Deployment agnosticity
	How to package ML models
		Serialized files
		Packetizing or containerizing
		Microservice generation and deployment
	Inference ready models
		Connecting to the workspace and importing model artifacts
		Loading model artifacts for inference
	Summary
Section 2: Deploying Machine Learning Models at Scale
Chapter 6: Key Principles for Deploying Your ML System
	ML in research versus production
		Data
		Fairness
		Interpretability
		Performance
		Priority
	Understanding the types of ML inference in production
		Deployment targets
		Mapping the infrastructure for our solution
	Hands-on deployment (for the business problem)
		Deploying the model on ACI
		Deploying the model on Azure Kubernetes Service (AKS)
		Deploying the service using MLflow
	Understanding the need for continuous integration and continuous deployment
	Summary
Chapter 7: Building Robust CI-CD Pipelines
	Continuous integration, delivery, and deployment in MLOps
		Continuous integration
		Continuous delivery
		Continuous deployment
	Setting up a CI-CD pipeline and the test environment (using Azure DevOps)
		Creating a service principal
		Installing the extension to connect to the Azure ML workspace
		Setting up a continuous integration and deployment pipeline for the test environment
		Connecting artifacts to the pipeline
		Setting up a test environment
	Pipeline execution and testing
	Pipeline execution triggers
	Summary
Chapter 8: APIs and Microservice Management
	Introduction to APIs and microservices
		What is an Application Programming Interface (API)?
		Microservices
	The need for microservices for ML
		Hypothetical use case
		Stage 1 – Proof of concept (a monolith)
		Stage 2 – Production (microservices)
	Old is gold – REST API-based microservices
	Hands-on implementation of serving an ML model as an API
		API design and development
	Developing a microservice using Docker
	Testing the API
	Summary
Chapter 9: Testing and Securing Your ML Solution
	Understanding the need for testing and securing your ML application
	Testing your ML solution by design
		Data testing
		Model testing
		Pre-training tests
		Post-training tests
	Hands-on deployment and inference testing (a business use case)
	Securing your ML solution by design
		Types of attacks
	Summary
Chapter 10: Essentials of Production Release
	Setting up the production infrastructure
		Azure Machine Learning workspace
		Azure Machine Learning SDK
	Setting up our production environment in the CI/CD pipeline
	Testing our production-ready pipeline
	Configuring pipeline triggers for automation
		Setting up a Git trigger
		Setting up an Artifactory trigger
		Setting up a Schedule trigger
	Pipeline release management
	Toward continuous monitoring
	Summary
Section 3: Monitoring Machine Learning Models in Production
Chapter 11: Key Principles for Monitoring Your ML System
	Understanding the key principles of monitoring an ML system
		Model drift
		Model bias
		Model transparency
		Model compliance
		Explainable AI
	Monitoring in the MLOps workflow
	Understanding the Explainable Monitoring Framework
		Monitor
		Analyze
		Govern
	Enabling continuous monitoring for the service
	Summary
Chapter 12: Model Serving and Monitoring
	Serving, monitoring, and maintaining models in production
	Exploring different modes of serving ML models
		Serving the model as a batch service
		Serving the model to a human user
		Serving the model to a machine
	Implementing the Explainable Monitoring framework
		Monitoring your ML system
		Analyzing your ML system
	Governing your ML system
	Summary
Chapter 13: Governing the ML System for Continual Learning
	Understanding the need for continual learning
		Continual learning
		The need for continual learning
	Explainable monitoring – governance
		Alerts and actions
		Model QA and control
		Model auditing and reports
	Enabling model retraining
		Manual model retraining
		Automated model retraining
	Maintaining the CI/CD pipeline
	Summary
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