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
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3. Experiment Tracking
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4. CI/CD for ML
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5. Model Monitoring
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6. Data Versioning
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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!