MLOps: Machine Learning Operations
The Reader's Dilemma
Dear Marilyn,My data science team builds great models in Jupyter notebooks, but when we try to deploy them to production, everything falls apart. How do the big tech companies manage to run thousands of ML models reliably?
Marilyn's Reply
The gap between a working notebook and a production system is vast. MLOps bridges this gap by applying DevOps principles to machine learning. It's not just about deploying models—it's about building systems that can train, deploy, monitor, and retrain models continuously and reliably.
The Spark: Understanding MLOps
The ML Lifecycle
Production ML isn't a one-time deployment—it's a continuous cycle of improvement.
The MLOps Cycle
Quick Check
What is the primary goal of MLOps?
Key MLOps Components
Version Control for ML
Track code, data, models, and experiments together.
Feature Stores
Centralized repository for feature definitions and values.
Model Registry
Central hub for model versioning, staging, and deployment.
Quick Check
What is a Feature Store used for?
Model Monitoring
Models degrade over time as the world changes. Monitoring detects problems before they impact users.
| Drift Type | What Changes | Detection Method |
|---|---|---|
| Data Drift | Input distribution | Statistical tests (KS, PSI) |
| Concept Drift | Relationship between inputs and outputs | Performance monitoring |
| Label Drift | Target distribution | Ground truth comparison |
Quick Check
What is 'concept drift' in machine learning?
CI/CD for ML
Continuous Integration and Deployment for ML extends traditional CI/CD with ML-specific stages.
ML Pipeline Stages
Quick Check
What is a 'canary release' in ML deployment?