Getting your models into production is the fundamental challenge of machine learning. MLOps offers a set of proven principles aimed at solving this problem in a reliable and automated way. This insightful guide takes you through what MLOps is (and how it differs from DevOps) and shows you how to put it into practice to operationalize your machine learning models.
Current and aspiring machine learning engineers–or anyone familiar with data science and Python–will build a foundation in MLOps tools and methods (along with AutoML and monitoring and logging), then learn how to implement them in AWS, Microsoft Azure, and Google Cloud. The faster you deliver a machine learning system that works, the faster you can focus on the business problems you’re trying to crack. This book gives you a head start.
You’ll discover how to:
- Apply DevOps best practices to machine learning
- Build production machine learning systems and maintain them
- Monitor, instrument, load-test, and operationalize machine learning systems
- Choose the correct MLOps tools for a given machine learning task
- Run machine learning models on a variety of platforms and devices, including mobile phones and specialized hardware
Preface Why We Wrote This Book How This Book Is Organized Chapters Appendix Exercise Questions Discussion Questions Origin of Chapter Quotes Conventions Used in This Book Using Code Examples O’Reilly Online Learning How to Contact Us Acknowledgements From Noah From Alfredo 1. Introduction to MLOps Rise of the Machine Learning Engineer and MLOps What is MLOps DevOps and MLOps A MLOps Hierarchy of Needs Implementing DevOps DataOps and Data Engineering Platform Automation MLOps 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 & Building Blocks Getting Started with Cloud Computing Python Crash Course Minimilistic 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 Conclusion Exercises Critical Thinking Discussion Questions 5. AutoML and KaizenML AutoML MLOps Industrial Revolution Kaizen vs KaizenML Feature Stores Apple’s Ecosystem Apple’s AutoML: CreateML Apple’s Core ML Tools Google’s AutoML and Edge Computer Vision Azure’s 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 MLOps on AWS MLOps Cookbook on AWS CLI Tools Flask Microservice AWS Lambda Recipes Deploy to AWS Lambda with SAM Applying AWS Machine Learning to the Real World Case Study: Sports Social Network Case Study: Career Advice with Julien Simon, AWS Machine Learning Evangelist 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 Retrieve 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 The Perfect Technique vs. The Real World Critical Challenges in MLOps Ethical and Unintended Consequences Lack of Operational Excellence Focus on Prediction Accuracy vs. the Big Picture Final Recommendations to Implement MLOPs Data Governance and Cybersecurity MLOps Design Patterns Conclusion Exercises Critical Thinking Discussion Questions A. Technology Certifications AWS Certifications AWS Cloud Practitioner and AWS Solutions Architect AWS Certified Machine Learning - Specialty Other Cloud Certifications Azure Data Scientist and AI Engineer GCP SQL Related Certifications B. Remote Work Equipment for Working Remote Network Home Work Area Location, Location, Location C. Think Like a VC for Your Career Pear Revenue Strategy Passive Positive Autonomy Exponential Rule of 25% NOTES D. Building a Technical Portfolio for MLOps Project: Continuous Delivery of Flask Data Engineering API Project: Docker & Kubernetes Container Project Project: Serverless AI Data Engineering Pipeline Project: Build Edge ML Solution Deliverables Project: Build Cloud-Native ML Application or API Project Selection Getting a Job: Don’t Storm the Castle, Walk in the backdoor E. Data Science Case Study: Intermittent Fasting Notes on Intermittent Fasting, Blood Glucose, and Food F. Key Terms G. Additional Educational Resources Additional MLOps Critical Thinking Questions Additional MLOps Educational Materials Education Disruption Current State of Higher Education That Will Be Disrupted Ten Times Better Education Conclusion H. Technical Project Management Project Plan Weekly Demo Task Tracking
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