MLOps Fundamentals: Pipelines, Monitoring, Versioning

Introduction: Automating the ML Lifecycle

You've deployed a model manually. Now imagine doing this weekly, or daily, with 50 models. MLOps (Machine Learning Operations) automates and standardizes the entire ML lifecycle.

MLOps = ML + DevOps + Data Engineering

Key Insight: Manual ML workflows don't scale. MLOps brings software engineering discipline to ML: version control, testing, CI/CD, and monitoring.

Learning Objectives

Understand MLOps principles and benefits
Set up ML pipelines
Implement CI/CD for ML
Track experiments and models
Automate training and deployment
Monitor models in production

1. MLOps Principles

Core Pillars

1. Automation

Data ingestion and validation
Model training and evaluation
Deployment and serving
Monitoring and retraining

2. Versioning

Code (Git)
Data (DVC, LakeFS)
Models (MLflow, Model Registry)
Environment (Docker, conda)

3. Continuous X

CI: Continuous Integration
CD: Continuous Deployment
CT: Continuous Training
CM: Continuous Monitoring

4. Monitoring

Data drift detection
Model performance degradation
Infrastructure health
Business metrics

2. ML Pipeline Architecture

3. Experiment Tracking

4. CI/CD for ML

5. Model Monitoring

6. Data Versioning

Key Takeaways

✅ MLOps automates the ML lifecycle from data to deployment

✅ Pipelines chain together data processing, training, evaluation, and deployment

✅ Experiment tracking enables comparing models and reproducibility

✅ CI/CD automates testing, training, and deployment

✅ Monitoring detects performance degradation and data drift

✅ Versioning tracks code, data, and models for reproducibility

What's Next?

Next lesson: Model Optimization – quantization, pruning, distillation, and inference acceleration!

Advanced ML: Unsupervised Learning & Production