From Notebook to Production: A 15-Day MLOps Learning Roadmap
_jaydeepkarale · x · 2026-08-25
This post outlines a 15-day MLOps learning plan designed to bridge the gap between running ML models in notebooks and deploying them as production systems. It covers the end-to-end lifecycle: Data → Features → Training → Evaluation → Deployment → Monitoring → Retraining.
Key topics include:
- MLOps fundamentals and maturity levels
- Data pipelines, feature stores, and validation
- Experiment tracking, reproducible training, and model registry
- Model evaluation, packaging, and serving
- CI/CD for ML and deployment strategies
- Monitoring, drift detection, and automated retraining
Tools covered: Git, DVC, MLflow, Kubeflow, Docker, Kubernetes, Feast, Airflow, Great Expectations, Evidently, Prometheus, Grafana, KServe, and BentoML.
More from Infra
- Developer seeks hosted agent harness for arbitrary tool integration — Disastrous_Gap_6473 · 2026-08-26
- Deep dive into CPU Optimizer Offload: Train 131k context on 32 GPUs — samsja19 · 2026-08-26
- prime-rl 0.9.0 ships adaptive concurrency, online agentic evals during SFT, CPU optimizer offload — samsja19 · 2026-08-26
- AI compresses chip design cycles but can't fix supply chain bottlenecks — saranormous · 2026-08-26
- Llama for Windows released: Run llama.cpp locally with Alt+Space shortcut — LysandreJik · 2026-08-26
- OpenAI product head: Future models will exceed laptop resources — haider1 · 2026-08-26