课程 · 12 · 10 / 12
MLOps Fundamentals: Pipelines, Monitoring, Versioning
Implement MLOps best practices: automated pipelines, model monitoring, versioning with DVC/MLflow, and experiment tracking.
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!
Further Reading
Free Courses & Hands-On Tutorials
- Made With ML — MLOps Course (Goku Mohandas) — the single best free, code-first MLOps course. Walks experiment → API → Docker → CI/CD → monitoring with real code.
- Full Stack Deep Learning — the legendary practitioner-focused course; 2022 edition is the most polished.
- Google Cloud — Practitioners Guide to MLOps — clearest one-document explanation of CT/CI/CD for ML.
Visual References
- MLOps Stack Template (Valohai) — landscape diagram of every MLOps tool category with examples.
- ml-ops.org — community-maintained reference with architecture diagrams and principles.
Papers & Articles
- Hidden Technical Debt in Machine Learning Systems — Sculley et al., Google, NIPS 2015. The foundational "ML systems are mostly not ML code" paper.
- Machine Learning: The High-Interest Credit Card of Technical Debt — the precursor; pair with the above.
- Reproducibility in ML: From Theory to Practice — Pineau et al., Nature 2021.
- Operationalizing Machine Learning: An Interview Study — Shankar et al., 2022. What ML engineers actually do day-to-day.
Documentation & Books
- Book: Designing Machine Learning Systems — Chip Huyen (O'Reilly 2022). The reference.
- Book: Reliable Machine Learning — Chen, Kreuzberger, Kühl, Hirschl (O'Reilly 2022). SRE perspective.
- MLflow, Weights & Biases, Neptune — the three major experiment-tracking platforms.
- Kubeflow, Metaflow, ZenML — modern pipeline orchestrators.
- DVC — data + model versioning; works on top of Git.