Practical MLOps Noah Gift, Alfredo Deza

Spis treści książki

• Preface
  • Why We Wrote This Book
  • How This Book Is Organized
    • Chapters
    • Appendixes
    • Exercise Questions
    • Discussion Questions
    • Origin of Chapter Quotes
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
    • From Noah
    • From Alfredo
• 1. Introduction to MLOps
  • Rise of the Machine Learning Engineer and MLOps
  • What Is MLOps?
  • DevOps and MLOps
  • An MLOps Hierarchy of Needs
    • Implementing DevOps
    • Configuring Continuous Integration with GitHub Actions
    • DataOps and Data Engineering
    • Platform Automation
    • MLOps
      • DevOps and MLOps combined best practices?
  • Conclusion
  • Exercises
  • Critical Thinking Discussion Questions
• 2. MLOps Foundations
  • Bash and the Linux Command Line
  • Cloud Shell Development Environments
  • Bash Shell and Commands
    • List Files
    • Run Commands
    • Files and Navigation
    • Input/Output
    • Configuration
    • Writing a Script
  • Cloud Computing Foundations and Building Blocks
  • Getting Started with Cloud Computing
  • Python Crash Course
  • Minimalistic Python Tutorial
  • Math for Programmers Crash Course
    • Descriptive Statistics and Normal Distributions
    • Optimization
  • Machine Learning Key Concepts
  • Doing Data Science
  • Build an MLOps Pipeline from Zero
  • Conclusion
  • Exercises
  • Critical Thinking Discussion Questions
• 3. MLOps for Containers and Edge Devices
  • Containers
    • Container Runtime
    • Creating a Container
    • Running a Container
    • Best Practices
    • Serving a Trained Model Over HTTP
  • Edge Devices
    • Coral
    • Azure Percept
    • TFHub
    • Porting Over Non-TPU Models
  • Containers for Managed ML Systems
    • Containers in Monetizing MLOps
    • Build Once, Run Many MLOps Workflow
  • Conclusion
  • Exercises
  • Critical Thinking Discussion Questions
• 4. Continuous Delivery for Machine Learning Models
  • Packaging for ML Models
  • Infrastructure as Code for Continuous Delivery of ML Models
  • Using Cloud Pipelines
    • Controlled Rollout of Models
    • Testing Techniques for Model Deployment
      • Automated checks
      • Linting
      • Continuous improvement
  • Conclusion
  • Exercises
  • Critical Thinking Discussion Questions
• 5. AutoML and KaizenML
  • AutoML
    • MLOps Industrial Revolution
    • Kaizen Versus KaizenML
    • Feature Stores
  • Apples Ecosystem
    • Apples AutoML: Create ML
    • Apples Core ML Tools
  • Googles AutoML and Edge Computer Vision
  • Azures AutoML
  • AWS AutoML
  • Open Source AutoML Solutions
    • Ludwig
    • FLAML
  • Model Explainability
  • Conclusion
  • Exercises
  • Critical Thinking Discussion Questions
• 6. Monitoring and Logging
  • Observability for Cloud MLOps
  • Introduction to Logging
  • Logging in Python
    • Modifying Log Levels
    • Logging Different Applications
  • Monitoring and Observability
    • Basics of Model Monitoring
    • Monitoring Drift with AWS SageMaker
    • Monitoring Drift with Azure ML
  • Conclusion
  • Exercises
  • Critical Thinking Discussion Questions
• 7. MLOps for AWS
  • Introduction to AWS
    • Getting Started with AWS Services
      • Using the No Code/Low Code AWS Comprehend solution
      • Using Hugo static S3 websites
      • Serverless Cookbook
      • AWS CaaS
      • Computer vision
    • MLOps on AWS
  • MLOps Cookbook on AWS
    • CLI Tools
    • Flask Microservice
      • Containerized Flask microservice
      • Automatically build container via GitHub Actions and push to GitHub Container Registry
      • AWS App Runner Flask microservice
  • AWS Lambda Recipes
    • AWS Lambda-SAM Local
    • AWS Lambda-SAM Containerized Deploy
  • Applying AWS Machine Learning to the Real World
  • Conclusion
  • Exercises
  • Critical Thinking Discussion Questions
• 8. MLOps for Azure
  • Azure CLI and Python SDK
  • Authentication
    • Service Principal
    • Authenticating API Services
  • Compute Instances
  • Deploying
    • Registering Models
    • Versioning Datasets
  • Deploying Models to a Compute Cluster
    • Configuring a Cluster
    • Deploying a Model
  • Troubleshooting Deployment Issues
    • Retrieving Logs
    • Application Insights
    • Debugging Locally
  • Azure ML Pipelines
    • Publishing Pipelines
    • Azure Machine Learning Designer
  • ML Lifecycle
  • Conclusion
  • Exercises
  • Critical Thinking Discussion Questions
• 9. MLOps for GCP
  • Google Cloud Platform Overview
    • Continuous Integration and Continuous Delivery
    • Kubernetes Hello World
    • Cloud Native Database Choice and Design
  • DataOps on GCP: Applied Data Engineering
  • Operationalizing ML Models
  • Conclusion
  • Exercises
  • Critical Thinking Discussion Questions
• 10. Machine Learning Interoperability
  • Why Interoperability Is Critical
  • ONNX: Open Neural Network Exchange
    • ONNX Model Zoo
    • Convert PyTorch into ONNX
    • Create a Generic ONNX Checker
    • Convert TensorFlow into ONNX
    • Deploy ONNX to Azure
  • Apple Core ML
  • Edge Integration
  • Conclusion
  • Exercises
  • Critical Thinking Discussion Questions
• 11. Building MLOps Command Line Tools and Microservices
  • Python Packaging
  • The Requirements File
  • Command Line Tools
    • Creating a Dataset Linter
    • Modularizing a Command Line Tool
  • Microservices
    • Creating a Serverless Function
    • Authenticating to Cloud Functions
    • Building a Cloud-Based CLI
  • Machine Learning CLI Workflows
  • Conclusion
  • Exercises
  • Critical Thinking Discussion Questions
• 12. Machine Learning Engineering and MLOps Case Studies
  • Unlikely Benefits of Ignorance in Building Machine Learning Models
  • MLOps Projects at Sqor Sports Social Network
    • Mechanical Turk Data Labeling
    • Influencer Rank
    • Athlete Intelligence (AI Product)
  • The Perfect Technique Versus the Real World
  • Critical Challenges in MLOps
    • Ethical and Unintended Consequences
    • Lack of Operational Excellence
    • Focus on Prediction Accuracy Versus the Big Picture
  • Final Recommendations to Implement MLOps
    • Data Governance and Cybersecurity
    • MLOps Design Patterns
  • Conclusion
  • Exercises
  • Critical Thinking Discussion Questions
• A. Key Terms
• B. Technology Certifications
  • AWS Certifications
    • AWS Cloud Practitioner and AWS Solutions Architect
    • AWS Certified Machine Learning - Specialty
      • Data Engineering
      • Exploratory Data Analysis (EDA)
      • Machine Learning Implementation and Operations (MLOps)
  • Other Cloud Certifications
    • Azure Data Scientist and AI Engineer
    • GCP
  • SQL-Related Certifications
• C. Remote Work
  • Equipment for Working Remotely
    • Network
      • Physical home network
      • Power management and home networking
    • Home Work Area
      • Health and work area
      • Home workspace virtual studio setup
    • Location, Location, Location
• D. Think Like a VC for Your Career
  • Pear Revenue Strategy
    • Passive
    • Positive
    • Exponential
    • Autonomy
    • Rule of 25%
    • Notes
• E. Building a Technical Portfolio for MLOps
  • Project: Continuous Delivery of Flask/FastAPI Data Engineering API on a PaaS Platform
  • Project: Docker and Kubernetes Container Project
  • Project: Serverless AI Data Engineering Pipeline
  • Project: Build Edge ML Solution
  • Project: Build Cloud Native ML Application or API
  • Getting a Job: Dont Storm the Castle, Walk in the Backdoor
• F. Data Science Case Study: Intermittent Fasting
  • Notes on Intermittent Fasting, Blood Glucose, and Food
• G. Additional Educational Resources
  • Additional MLOps Critical Thinking Questions
  • Additional MLOps Educational Materials
  • Education Disruption
    • Current State of Higher Education That Will Be Disrupted
    • 10X Better Education
      • Built-in apprenticeship
      • Focus on the customer
      • Lower time to completion
      • Lower cost
      • Async and remote first
      • Inclusion first versus exclusion first
      • Nonlinear versus serial
      • Life-long learning: permanent access to content for alumni with continuous upskill path
      • Regional job market that will be disrupted
      • Disruption of hiring process
  • Conclusion
• H. Technical Project Management
  • Project Plan
  • Weekly Demo
  • Task Tracking
• Index

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