Visualizing and Alerting on AI Performance KPIs

As artificial intelligence models transition from research to production, maintaining their reliability and performance is critical. This text-based course introduces you to the essential principles of tracking, visualizing, and alerting on AI performance indicators. You will learn how to move beyond basic evaluation to establish robust, real-time monitoring systems that protect your applications from unexpected failures. In this course, you will: Learn the foundational terminology of AI performance, including latency, throughput, accuracy, and cost tracking; Understand how to detect data drift and concept drift to maintain model accuracy over time; Design clear visual dashboards that represent complex operational and statistical metrics; Configure alert systems and thresholds to notify teams when performance degrades; Apply modern observability patterns to generative AI applications, tracking token usage and prompt latency. Starting with core definitions, you will read through step-by-step guides and practical scenarios to build a solid foundation in AI monitoring. This course is ideal for beginners, aspiring data analysts, and product managers looking to understand AI system health with no prior advanced engineering experience. Start reading today to master the fundamentals of AI observability and performance alerting.