MLOps Pro

Expert-level orchestration of machine learning systems. Focuses on bridging the gap between data science and operations to ensure reliable, scalable AI deployment.

Boundary

mlops-pro covers the operational side of AI: data versioning (DVC), model registries (MLflow), feature stores (Feast), and serving (Triton, Seldon). It does NOT cover model research or deep mathematical training logic.

When to use

• Designing a CI/CD pipeline for machine learning models.
• Implementing model monitoring and drift detection.
• Setting up a centralized feature store for a data team.
• Architecting a scalable model serving infrastructure on Kubernetes.

Workflow