MLOps Engineer
A research-backed roadmap from foundations to production-grade ML systems — Docker, Kubernetes, MLflow, feature stores, model serving with vLLM, and monitoring in the exact dependency order.
Foundations
2–3 weeksSoftware engineering fundamentals, ML literacy, Linux/Bash, GitOps, and cloud basics — the prerequisite layer before any MLOps tooling makes sense.
Containerisation & Infrastructure
3–4 weeksDocker, Kubernetes, Helm, and Infrastructure as Code — the foundational layer that everything else in MLOps runs on.
ML Experiment Tracking & Versioning
2–3 weeksMLflow, W&B, DVC, and Hydra — the reproducibility infrastructure layer. If your experiments aren't tracked, they didn't happen.
ML Pipelines & Orchestration
3–4 weeksAirflow, Prefect, Kubeflow, and cloud-native pipeline platforms — turning one-off training scripts into repeatable, observable, production-grade workflows.
Feature Stores & Data Management
2–3 weeksFeast, Tecton, and online/offline store architecture — preventing training-serving skew, the most insidious silent failure in production ML.
Model Serving & Deployment
3–4 weeksFastAPI, BentoML, Ray Serve, Triton, and vLLM — building low-latency, high-throughput, production-grade inference systems that scale.
Monitoring & Observability
2–3 weeksEvidently AI, NannyML, Prometheus/Grafana for ML, and LLM observability with LangSmith — detecting silent model degradation before it affects business metrics.
CI/CD for ML & Career
3–4 weeksGitHub Actions for ML, CML for GitOps model evaluation, ML system design interviews, and the certifications that differentiate MLOps engineers in 2026.
Ready to build production ML systems?
Docker and Kubernetes are the foundation — everything else in MLOps runs on top of them.