Monitoring And Maintaining Genai Systems

Monitor GenAI systems, detect drift, reduce hallucinations, apply MLOps, and ensure reliable AI performance

What you'll learn

Interpret system and model metrics to monitor GenAI behavior

Detect and respond to model drift and hallucinations

Use Prometheus and Weights & Biases for observability

Build audit trails and align monitoring with governance

Apply MLOps and DevOps strategies to GenAI operations

Understand how GenAI performance affects business outcomes

Requirements

Basic experience with GenAI systems required; basic familiarity with software systems and AI concepts is helpful but not mandatory.

Description

Generative AI systems are powerful, dynamic, and increasingly integrated into everyday business operations — but they are also unpredictable, complex to monitor, and difficult to maintain over time. This course is designed to help you build the skills and mindset needed to monitor, evaluate, and maintain GenAI systems in live production environments.In this course, you’ll learn how to interpret and act on key performance signals such as latency, throughput, token usage, hallucination rate, and user feedback. You’ll explore how to design observability layers that go beyond traditional metrics — integrating both infrastructure-level monitoring (with tools like Prometheus and Grafana) and model-centric monitoring (with Weights & Biases).We’ll also walk through structured approaches to identifying and responding to issues like model drift, prompt failure, or quality degradation. You’ll understand how to align system health with business outcomes, and how to ensure your GenAI assistant stays relevant, reliable, and trustworthy over time.To make the learning practical and grounded, you’ll follow the story of InsightBot, a GenAI system developed by a fictional company — GenPrompt Solutions Inc. You’ll see how InsightBot is monitored, audited, updated, and optimized as part of an ongoing system lifecycle.By the end of this course, you’ll understand how to implement logging and audit trails, automate retraining and deployment cycles, and use feedback loops to support continuous improvement. You’ll also gain awareness of best practices in MLOps and DevOps for GenAI, and how to connect technical observability with ethical AI governance and business strategy.This course is ideal for data scientists, machine learning engineers, AI architects, DevOps professionals, and technical leads working with GenAI systems. No prior experience with monitoring tools is required — the course will guide you step by step.If you're ready to move from building GenAI systems to running them confidently and responsibly, this course is your next step.

Overview

Section 1: Introduction

Lecture 1 Introduction

Section 2: Introduction to GenAI System Monitoring

Lecture 2 Introduction to GenAI System Monitoring (1)

Lecture 3 Introduction to GenAI System Monitoring (2)

Section 3: Use Case Overview – InsightBot at GenPrompt Solutions Inc

Lecture 4 Use Case Overview – InsightBot at GenPrompt Solutions Inc (1)

Lecture 5 Use Case Overview – InsightBot at GenPrompt Solutions Inc (2)

Section 4: Key Metrics for Monitoring GenAI Systems

Lecture 6 Key Metrics for Monitoring GenAI Systems

Section 5: Monitoring Tools and Infrastructure

Lecture 7 Monitoring Tools and Infrastructure

Section 6: Evaluating and Debugging Model Performance

Lecture 8 Evaluating and Debugging Model Performance

Section 7: Logging and Auditing GenAI Systems

Lecture 9 Logging and Auditing GenAI Systems

Section 8: Retraining and Updating GenAI Models

Lecture 10 Retraining and Updating GenAI Models

Section 9: MLOps and DevOps for GenAI Systems

Lecture 11 MLOps and DevOps for GenAI Systems

Section 10: Case Study – Monitoring InsightBot with Weights & Biases

Lecture 12 Case Study – Monitoring InsightBot with Weights & Biases

Section 11: Best Practices and Future Trends

Lecture 13 Best Practices and Future Trends

Section 12: Conclusion

Lecture 14 Conclusion

This course is ideal for data scientists, machine learning engineers, software developers, DevOps professionals, and AI system architects responsible for maintaining GenAI systems.,It’s also valuable for product managers and technical leaders looking to understand GenAI system health, observability, and long-term maintenance strategies.