If model rollouts feel risky, monitoring is an afterthought, and updates make you nervous, you’re not alone. As AI moves from prototype to production, the stakes rise: model supply chains, promotion workflows, and runtime behavior need guardrails, not just good intentions. This course is your blueprint for shipping with confidence by baking security into every phase of the AI Model lifecycle. You’ll learn to choose the right deployment strategy for your risk profile, enforce provenance and approvals with a model registry, and wire continuous monitoring for data/feature drift, performance, and safety signals. We also cover securing updates with signed artifacts, CI/CD policy gates, and rapid, auditable rollback.

ML engineers, MLOps practitioners, and DevOps teams work together to ensure AI models move smoothly from development to production. ML engineers focus on building and training models, MLOps practitioners streamline and automate the model lifecycle, and DevOps teams manage infrastructure and deployment. Together, they create a reliable, scalable, and efficient pipeline for delivering AI solutions that perform consistently in real-world environments. Git & CI/CD basics, Docker or managed ML platform experience, working knowledge of Python ML workflows and environment/package management. By the end, you’ll ship behind structured change control, track lineage from dataset to container, and respond quickly when reality (or your threat model) changes. Whether you run on Kubernetes, serverless, or managed ML platforms, the practical flows, templates, and hands-on exercises in this course help you harden deployments without slowing delivery; turning ad-hoc launches into repeatable, secure lifecycles from commit to canary to continuous oversight.