Overview

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.

Syllabus

Secure Deployment Strategies for AI Services

In this module, Learners compare rollout patterns, including shadow, canary, and blue/green based on risk, observability, and rollback needs. They then implement a quick canary with AWS Lambda aliases to practice traffic shifting, gating, and instant rollback. Learners will also apply this knowledge in a live canary rollout using AWS Lambda, implementing traffic splitting, gating, and rollback in response to safety or performance regressions.

Model Registry Management and Promotion Governance

In this module, learners will design and implement a registry-centered promotion flow for AI models. They will learn to capture versioning and lineage, move model versions through different stages, and attach necessary evidence and approvals at each stage. Learners will then apply this process in a CI/CD pipeline, enforcing security with signed artifacts and SBOM checks to ensure that only verified and approved versions are deployed to production.

Lifecycle Monitoring & Securing Model Updates

In this module, learners will learn how to operate AI services safely in production. They will develop the skills to set up effective monitoring for key metrics such as latency, errors, drift, and safety. Learners will also learn how to interpret these metrics and connect them to actionable operational decisions. Additionally, they will explore secure update practices, including how to use signed artifacts, SBOM-based scanning, CI/CD policy gates, and audit trails to ensure safe, auditable, and controlled releases.