Phase 1: Foundations
Python, Linux, Git, basic ML
Phase 2: DevOps & Infrastructure
Docker, Kubernetes, Terraform, CI/CD
Phase 3: ML Engineering
Model training, evaluation, experiment tracking
Phase 4: MLOps Core
CI/CD for ML, model versioning, monitoring, pipelines
Phase 5: Production LLM Infrastructure
Inference serving, quantization, scaling
MLOps Tools Stack (2026)
| Category | Tools |
|---|---|
| Experiment Tracking | MLflow, W&B, Neptune |
| Data Versioning | DVC, LakeFS |
| Model Registry | MLflow, SageMaker, Vertex AI |
| Orchestration | Airflow, Prefect, Dagster |
| Serving | vLLM, TensorRT, Triton, BentoML |
| Monitoring | Prometheus, Grafana, Evidently AI |
| Infrastructure | Docker, K8s, Terraform, ArgoCD |
FAQ
What is MLOps?
MLOps combines ML engineering, DevOps, and data engineering to deploy, monitor, and maintain ML models in production reliably.
What salary can an MLOps engineer expect?
US: Entry $100-140K, Mid $150-200K, Senior $220-320K. High demand because most companies struggle to productionize models.
Is MLOps different from DevOps?
Yes. MLOps extends DevOps with data versioning, experiment tracking, model registries, drift detection, and retraining pipelines.