MLOps and DevOps
MLOps (Machine Learning Operations) bridges the gap between model development and production deployment. It applies DevOps principles to machine learning systems, ensuring reliable, scalable, and maintainable AI applications.1
Topics in This Section
| Topic | Description |
|---|---|
| ML Pipelines | End-to-end workflow automation |
| Experiment Tracking | MLflow, Weights & Biases, experiment management |
| Model Serving | APIs, vLLM, Triton, model deployment |
| Containerization | Docker, Kubernetes for ML workloads |
| CI-CD for ML | Continuous integration and deployment |
| Infrastructure as Code | Terraform, Bicep, reproducible infrastructure |
| Monitoring & Observability | Model drift, performance tracking, alerting |
Learning Path
Containerization → ML Pipelines → Experiment Tracking → Model Serving → CI-CD → Monitoring
Related Domains
- Building ML models? See 02 - Machine Learning
- Deploying LLMs? See 05 - Generative AI
- Cloud infrastructure? See Infrastructure as Code
References
Sculley, D., et al. (2015). Hidden Technical Debt in Machine Learning Systems. Advances in Neural Information Processing Systems. https://papers.nips.cc/paper/2015/hash/86df7dcfd896fcaf2674f757a2463eba-Abstract.html ↩︎