The difference between GenAI systems that fail in production and those that scale reliably? Systematic deployment orchestration and evaluation practices that prevent costly failures before they impact users.
This Short Course was created to help ML and AI professionals accomplish robust, production-ready GenAI deployments with built-in reliability and automated recovery mechanisms. By completing this course, you'll be able to proactively identify deployment compatibility issues through manifest analysis, make data-driven release decisions using observability dashboards and regression test results, and implement sophisticated canary deployment workflows that automatically rollback when performance metrics degrade—skills you can apply immediately in your next GenAI production deployment. By the end of this course, you will be able to: • Analyze deployment manifests and dependencies to ensure runtime compatibility • Evaluate release readiness using regression test results and observability dashboards • Create an orchestrated deployment workflow with integrated canary releases and automated rollbacks This course is unique because it combines hands-on deployment analysis with real-world production scenarios, teaching you to build the resilient deployment systems that modern GenAI operations demand. To be successful in this course, you should have a background in machine learning systems, containerization technologies, and basic understanding of production deployment practices.
