15 min read
Lesson 1 of 5
MLOps Foundations: From Notebook to Production
From the course AI Infrastructure & MLOps
MLOps (Machine Learning Operations) bridges the gap between data science experiments and production systems. It's DevOps for AI — the practices, tools, and culture that make ML systems reliable, reproducible, and maintainable.
The ML Lifecycle
- Data: Collection, cleaning, labeling, versioning
- Experimentation: Model training, hyperparameter tuning, evaluation
- Deployment: Serving infrastructure, API design, A/B testing
- Monitoring: Performance tracking, drift detection, alerting
- Iteration: Feedback loops, retraining, continuous improvement
Why ML Systems Are Different
Traditional software is deterministic — same code, same behavior. ML systems have additional complexity:
- Data dependencies: Models are only as good as their training data, which changes over time
- Concept drift: The real-world patterns your model learned can shift
- Reproducibility: "It works on my machine" is even worse when GPUs, random seeds, and data versions are involved
- Testing: You can't write unit tests for "good enough" predictions
- Technical debt: ML-specific debt accumulates in data pipelines, feature stores, and model management
MLOps Maturity Levels
| Level | Description | Characteristics |
|---|---|---|
| 0 — Manual | Everything done by hand | Jupyter notebooks, manual deployment, no monitoring |
| 1 — Automated training | Training pipelines automated | Version-controlled experiments, automated retraining |
| 2 — CI/CD for ML | Full automation with quality gates | Automated testing, staged rollouts, monitoring |
| 3 — Continuous operations | Self-healing, self-improving | Automatic drift detection, auto-retraining, A/B testing |
Most teams are at Level 0 or 1. Getting to Level 2 should be your near-term goal.
Key Tools
- Experiment tracking: MLflow, Weights & Biases, Neptune
- Data versioning: DVC, LakeFS
- Model registry: MLflow Model Registry, Hugging Face Hub
- Orchestration: Airflow, Prefect, Dagster, Kubeflow Pipelines
- Feature stores: Feast, Tecton
- Serving: vLLM, TGI, Triton, BentoML
Key Takeaways
- •MLOps applies DevOps principles to the unique challenges of ML systems
- •ML systems have data dependencies and concept drift that traditional software doesn't
- •Aim for MLOps Level 2 (CI/CD for ML) as your near-term maturity target
- •The ML lifecycle spans data, experimentation, deployment, monitoring, and iteration
0 of 5 lessons marked complete
Progress is kept in this browser only.