LLMOps Evaluation, Observability, and Quality

Generative AI systems require rigorous evaluation and monitoring. This course will teach you how to evaluate, test, observe, and continuously monitor GenAI systems using metrics, automated testing, logging, dashboards, and drift detection.

What you'll learn

Building reliable, production-grade generative AI systems requires more than strong models—it demands rigorous evaluation, testing, observability, and monitoring practices. In this course, LLMOps: Evaluation, Observability, and Quality, you'll gain the ability to design, implement, and operate robust evaluation and observability frameworks for large language model-based and multimodal AI systems. First, you'll explore how to evaluate LLM and multimodal outputs using automated metrics, human evaluation, and multidimensional quality frameworks aligned with real production use cases. Next, you'll discover how to implement observability, logging, and continuous evaluation pipelines that track performance, cost, safety, and quality over time. Finally, you'll learn how to apply automated testing, drift detection, and monitoring strategies to detect regressions, manage model updates, and ensure long-term system reliability. When you're finished with this course, you'll have the skills and knowledge of generative AI evaluation and monitoring needed to confidently deploy, operate, and scale GenAI systems in production environments.

Homepage

https://www.pluralsight.com/courses/llmops-evaluation-observability-quality

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