Practical MLOPS HOW TO GET READY FOR PRODUCTION MODELS WITH CHAPTERS FROM 2 Table of Contents Contents Why MLOps Matters? (Foreword) People of Machine Learning 3 5 How Is Machine Learning Different from Traditional Software? The MLOps Workflow 8 10 What Is the Point of MLOps? 10 Risk in Machine Learning 11 Time to Market 15 The MLOps Workflow Enforces Best Practices How to Quantify Success in an MLOps Project? Speak the Same Language 18 20 22 Use Every Project as an Opportunity to Educate Your Organization about Machine Learning 22 Define Clear Shared Objective and Metrics 22 Real-World Example - The Story of Two Companies Company 1 25 Company 2 26 Learnings 26 The MLOps Toolchain 28 Model and Data Exploration 28 Metrics and Model Optimization 31 Productionalization - End-to-End Pipelines 35 Productionalization - Feature Stores 39 Testing 43 Deployment and Inference Conclusion 50 About the Authors 52 46 25 3 Why MLOps Matters? Why MLOps Matters? (Foreword) Traditionally, machine learning has been approached from a perspective of individual scientific experiments which are predominantly carried out in isolation by data scientists. However, as machine learning models become part of real-world solutions and critical to business, we will have to shift our perspective, not to depreciate scientific principles but to make them more easily accessible, reproducible and collaborative. In May 2020, we surveyed 330 data scientists, machine learning engineers and managers in a broad range of companies to ask what they were focused on for the next 3 months and what major obstacles they were faced with. Although 20% of respondents said they are still focusing more on the experimentation and learning phase, half of the respondents said that they were focused on developing models for production use and over 40% said they would be deploying models for production. Source: State of ML 2020, Valohai 330 respondents Collect more data Figure out ho