Accelerated Innovation

Supporting Your GenAI Solution

GenAI Monitoring & Alerting Best Practices

Workshop
Do you know when your GenAI systems are drifting, failing, or slowing down—before users do?

GenAI pipelines introduce new reliability risks that traditional monitoring often misses, from silent quality drift to latency spikes and cascading failures. Effective monitoring and alerting are required to keep GenAI systems observable, responsive, and production-ready.

To win, your GenAI solutions must be continuously observable, proactively alerting, and supported by clear incident response practices.

The Challenge

When GenAI monitoring and alerting are insufficient, teams struggle to maintain reliability:

  • Undefined reliability signals: Teams lack clear metrics that reflect GenAI health, quality, and performance.
  • Delayed failure detection: Drift, latency, and pipeline failures go unnoticed until users are impacted.
  • Fragmented operational response: Incidents are handled reactively without clear workflows or visibility.

These gaps lead to prolonged outages, degraded user trust, and slow recovery from GenAI incidents.

Our Solution

In this hands-on workshop, your team designs and implements practical monitoring and alerting patterns tailored to GenAI systems.

  • Define monitoring metrics that accurately represent GenAI reliability and behavior.
  • Configure alerts for drift, failures, and latency across GenAI pipelines.
  • Visualize logs, metrics, and trends in real time to support rapid diagnosis.
  • Establish incident response practices specific to GenAI operational failures.
  • Automate monitoring pipelines across tools to ensure consistent coverage.
Area of Focus
  • Defining Monitoring Metrics for GenAI Reliability
  • Setting Up Alerts for Drift, Failures, and Latency
  • Visualizing Logs and Trends in Real-Time
  • Establishing Incident Response for GenAI Pipelines
  • Automating Monitoring Pipelines Across Tools
Participants Will
  • Identify and apply the right metrics to monitor GenAI system health.
  • Detect drift, failures, and latency issues before they impact users.
  • Use real-time visualizations to diagnose GenAI issues quickly.
  • Respond to GenAI incidents with clear, repeatable operational workflows.
  • Automate monitoring to reduce manual effort and blind spots.

Who Should Attend:

ML EngineersPlatform EngineersSite Reliability EngineersOperations LeadersEngineering Managers

Solution Essentials

Format

Facilitated workshop (in-person or virtual) 

Duration

4 hours 

Skill Level

Intermediate 

Tools

Monitoring, logging, alerting, and incident management tooling in a guided environment

Can your team detect GenAI failures and drift before they reach users?