Accelerated Innovation

Supporting Your GenAI Solution

GenAI Reliability, Resilience, & DR Best Practices

Workshop
Are your GenAI systems designed to survive failures, outages, and unexpected load without impacting users?

As GenAI workloads move into production, reliability risks expand beyond models to infrastructure, pipelines, and dependencies. Without explicit resilience and disaster recovery design, failures become costly, prolonged, and difficult to recover from.

To win, your GenAI solutions must be built with measurable reliability, fault tolerance, and tested recovery paths.

The Challenge

When GenAI reliability and resilience are underdeveloped, teams face preventable outages and operational risk:

  • Undefined reliability targets: Teams lack clear metrics and KPIs that define acceptable GenAI system behavior.
  • Fragile system design: GenAI pipelines are not engineered for fault tolerance or high availability.
  • Untested recovery paths: Disaster recovery plans exist on paper but fail under real failure conditions.

These weaknesses lead to prolonged downtime, user impact, and loss of confidence in GenAI services.

Our Solution

In this hands-on workshop, your team applies reliability and resilience best practices to harden GenAI systems against real-world failures.

  • Define GenAI reliability metrics and KPIs aligned to service expectations.
  • Design architectures that support fault tolerance and high availability.
  • Implement disaster recovery plans tailored to GenAI workloads.
  • Simulate failure scenarios to validate resilience and recovery behavior.
  • Embed reliability practices directly into deployment and monitoring workflows.
Area of Focus
  • Defining GenAI Reliability Metrics and KPIs
  • Designing for Fault Tolerance and High Availability
  • Implementing Disaster Recovery Plans for GenAI Workloads
  • Simulating Failure Scenarios and Resilience Testing
  • Embedding Reliability into Deployment and Monitoring
Participants Will
  • Defining GenAI Reliability Metrics and KPIs
  • Designing for Fault Tolerance and High Availability
  • Implementing Disaster Recovery Plans for GenAI Workloads
  • Simulating Failure Scenarios and Resilience Testing
  • Embedding Reliability into Deployment and Monitoring

Who Should Attend:

ML EngineersPlatform EngineersOperations LeadersEngineering Managers MLOps / LLMOps Engineers

Solution Essentials

Format

Facilitated workshop (in-person or virtual) 

Duration

4 hours 

Skill Level

Intermediate 

Tools

Infrastructure platforms, monitoring systems, and resilience testing tools in a guided environment

Is your GenAI platform resilient enough to withstand real production failures?