Monitoring, Logging, and Observability for GenAI Systems

Track AI usage, errors, and outcomes for reliability and auditability.

Pillar

Technology – Platforms, Tools, Infrastructure & Productivity

Overview

This course equips participants with the knowledge and skills to implement comprehensive monitoring, logging, and observability frameworks tailored for Generative AI systems. Attendees will learn how to track AI model performance, detect anomalies, ensure system reliability, and maintain audit trails for compliance and continuous improvement. The course covers best practices for integrating monitoring tools into GenAI deployments and interpreting data to drive actionable insights.

Learning Objectives

Participants will be able to:

• Design monitoring and logging strategies specific to GenAI workflows

• Implement observability tools to capture model usage, performance, and errors

• Analyze logs and metrics to detect issues and optimize AI outputs

• Establish audit trails for regulatory compliance and governance

• Use monitoring insights to support continuous model tuning and business decisions

Target Audience

• AI/ML engineers and data scientists

• DevOps and IT operations teams

• Compliance and risk managers

• AI program managers and technical leads

Duration

20 hours over 4 days (5 hours per day)

Delivery Format

• Interactive lectures with real-world examples

• Hands-on workshops setting up monitoring and logging tools

• Case studies on GenAI observability challenges

• Group discussions and problem-solving sessions

Materials Provided

• Sample monitoring frameworks and templates

• Access to monitoring tools and dashboards

• Guidelines for compliance-related logging

• Troubleshooting checklists for GenAI systems

Outcomes

• Ability to implement effective monitoring and logging for GenAI

• Improved system reliability and faster issue resolution

• Enhanced compliance through detailed audit records

• Data-driven approach to GenAI model maintenance and upgrades

Outline / Content

Day 1: Fundamentals of Monitoring and Logging for GenAI

• Key concepts: observability, telemetry, and AI-specific metrics

• Overview of monitoring tools and platforms

Day 2: Implementing Observability in GenAI Systems

• Instrumenting AI pipelines for data capture

• Setting up real-time dashboards and alerts

Day 3: Analyzing Logs and Metrics for Reliability

• Root cause analysis of AI failures and anomalies

• Performance tuning based on monitoring insights

Day 4: Compliance, Auditing, and Continuous Improvement

• Creating audit trails for AI usage and decision-making

• Using observability data to inform governance and model updates