Did you know that 85% of organizations deploying generative AI systems experience significant performance degradation within the first six months due to inadequate monitoring and governance? As AI becomes mission-critical for business operations, the ability to maintain consistent, high-quality outputs while managing risks has become one of the most sought-after skills in the industry.

This Short Course was created to help AI practitioners, machine learning engineers, and technical leaders accomplish the critical task of running powerful generative AI systems reliably and responsibly in production environments. By completing this course you'll be able to immediately implement performance monitoring dashboards for your AI systems, make data-driven decisions about model optimization strategies, and establish governance frameworks that protect your organization from AI-related risks while maintaining innovation velocity. By the end of this course, you will be able to: Analyze prompt performance metrics across user cohorts to identify drift in response quality and implement corrective measures. Evaluate trade-offs between fine-tuning and retrieval-augmented generation approaches to make strategic technical decisions for new domains. Create comprehensive governance frameworks with enforceable policies and technical guardrails for generative AI outputs. Lead cross-functional teams in AI system reviews and recommend optimization strategies to product leadership. Design and implement monitoring systems that ensure consistent AI performance across diverse user populations. This course is unique because it combines hands-on technical skills with strategic business thinking, focusing on real-world production challenges rather than theoretical concepts. You'll work with actual performance data, conduct live system evaluations, and create governance documents that can be immediately implemented in your organization. To be successful in this course, you should have a background in machine learning fundamentals, basic understanding of large language models, experience with data analysis and metrics interpretation, and familiarity with software development practices in AI/ML environments