GenAI Monitoring: MLOps, Model Drift & LLM Observability

This course contains the use of artificial intelligence.

Led by Dr. Amar Massoud, a seasoned expert with decades of academic and professional experience, it combines cutting-edge AI support with human insight to deliver content that is precise, practical, and easy to follow. You’ll gain the clarity of structured learning and the confidence of being guided by a recognized authority.

Generative AI systems are transforming how organizations operate, but they are also complex, unpredictable, and highly dynamic. Building them is only the first step—monitoring and maintaining them in production is where the real challenge begins. This course equips you with the knowledge and mindset to ensure GenAI systems remain reliable, efficient, and aligned with both technical and business goals.

You will learn how to interpret critical system and model metrics, including latency, throughput, token usage, hallucination rates, and feedback signals. These metrics form the foundation of robust observability practices, helping you detect early warning signs and maintain system trustworthiness.

The course also introduces industry-leading monitoring tools. You will explore how Prometheus and Grafana are used for infrastructure-level monitoring, and how Weights & Biases (W&B) supports LLM tracking, drift detection, and performance visualization. Together, these tools enable a layered approach to system reliability.

Beyond metrics and tools, you will understand how to structure effective monitoring strategies. This includes diagnosing and addressing model drift, maintaining audit trails, handling sensitive data responsibly, and ensuring that monitoring aligns with governance and compliance frameworks. You’ll also discover how MLOps and DevOps principles apply to generative AI, from CI/CD pipelines for prompt updates to incident management workflows.

To anchor the learning, the course uses a model company—GenPrompt Solutions Inc.—and its GenAI assistant, InsightBot. This case study demonstrates how monitoring practices come together in a realistic organizational context, showing how technical signals connect to user experience and business impact.

By the end of the course, you will have a structured understanding of GenAI observability, the confidence to evaluate system performance, and the foresight to anticipate and respond to emerging challenges in AI operations.

If you are a data scientist, AI engineer, machine learning practitioner, DevOps professional, or technical leader looking to maintain GenAI systems effectively, this course is for you.