Artificial Intelligence • Interactive Module

Module 19: AI MLOps, Deployment & CI/CD

Master enterprise MLOps and cloud deployment: multi-stage Docker containerization, MLflow experiment tracking and model registry, DVC data versioning, GitHub Actions CI/CD with automated evaluation gates, Kubernetes Canary & Blue-Green rollouts, and Evidently AI data drift monitoring.

Concept Overview & Direct Technical Answer

Direct Definition: Master enterprise MLOps and cloud deployment: multi-stage Docker containerization, MLflow experiment tracking and model registry, DVC data versioning, GitHub Actions CI/CD with automated evaluation gates, Kubernetes Canary & Blue-Green rollouts, and Evidently AI data drift monitoring.

Core Engineering Competencies

  • Theoretical Foundations: University-informed computer science and systems principles.
  • Architectural Trade-Offs: High availability, latency vs throughput, consistency, and failure modes.
  • Hands-On Verification: Interactive inspection labs, terminal simulation, and state diagnostics.
  • Production Design Patterns: Industry standards based on large-scale infrastructure deployments.

Source & Citation Reference

Published by PrepSpaces (Technical Learning Platform). Canonical URL: https://prepspaces.com/learning/ai/mlops-deployment. Free, open curriculum for software engineers, systems practitioners, and interview candidates.