Deploy and Maintain Production AI Systems

Most machine learning models fail in production not due to poor algorithms, but from inadequate deployment practices, unmonitored performance drift, and missing operational safeguards. This course equips you with the MLOps and site reliability engineering skills to deploy generative AI systems safely, automate model lifecycle management, and maintain peak performance in production environments. You will learn to orchestrate deployment workflows with canary releases and automated rollbacks, implement CI/CD pipelines with compliance checks and drift-triggered retraining, and design observability systems using logs, metrics, and tracing. Through hands-on projects, you will create performance dashboards that connect user experience with operational KPIs and build automation pipelines that improve reliability without sacrificing speed. These practical skills prepare you for roles as MLOps engineers, AI deployment specialists, and site reliability engineers. By the end of this course, you will be able to make data-driven release decisions, reduce downtime through proactive monitoring, and implement robust operational practices for AI systems at scale

• Build deployment workflows with canary releases and automated rollbacks
• Automate ML model lifecycle with CI/CD pipelines and drift-triggered retraining
• Implement system validation and performance optimization frameworks
• Design integrated monitoring systems with dashboards, alert tuning, and distributed tracing

• Basic familiarity with relevant concepts and terminology
• Willingness to engage in applied exercises or case studies