LLMs introduce new operational realities—behavior drift, usage spikes, and cost variability. This workshop defines LLM-specific ops practices for monitoring, incident response, and lifecycle management so reliability improves and spend stays controlled.
Leave with a practical LLM ops approach that improves reliability, reduces incident impact, and keeps cost predictable as adoption grows.
Many teams apply standard ops practices to LLMs—then discover they don’t cover the realities of model behavior, usage variability, and cost dynamics.
- Operational requirements and constraints are unclear: Teams lack shared expectations for availability, latency, acceptable behavior, and escalation paths—creating fragile operations.
- Deployment and versioning workflows are inconsistent: Without repeatable workflows, changes introduce regressions and teams struggle to compare performance over time.
- Monitoring and incident response aren’t designed for LLM behavior: Usage spikes, performance degradation, and behavioral anomalies aren’t detected early, slowing response and eroding trust.
If LLM ops isn’t designed intentionally, reliability and cost become the limiter to GenAI scale.
We help teams operationalize LLM ops as an enterprise capability—clear requirements, disciplined release practices, and monitoring that catches issues early.
- Define LLM-specific operational requirements and constraints: Establish expectations for performance, reliability, usage patterns, and safe operating bounds.
- Implement workflows for deployment and versioning: Define repeatable processes that reduce regressions and support comparable performance tracking over time.
- Monitor usage, performance, and behavior anomalies: Identify the signals that matter for LLM operations so issues are detected before users feel impact.
- Respond with structured operational plans: Create incident response approaches that reduce time-to-recovery and make response less dependent on a few experts.
- Optimize lifecycle management and cost efficiency: Define practices to manage ongoing changes while keeping cost and operational effort under control.
- LLM-specific operational requirements and constraints
- Workflows for model deployment and versioning
- Monitoring usage patterns
- Monitoring performance and latency
- Monitoring behavior anomalies
- Incident response with structured operational plans
- LLM lifecycle management and cost efficiency
- Define the operational requirements and constraints needed to run LLMs as enterprise services
- Establish deployment and versioning workflows that reduce regressions and improve comparability
- Identify monitoring signals to detect usage spikes, performance issues, and behavioral anomalies early
- Create structured incident response plans that reduce downtime and restore confidence quickly
- Leave with lifecycle and cost optimization practices to support scaling without runaway spend
Who Should Attend:
Solution Essentials
Facilitated workshop (interactive discussion + working session)
8 hours
Advanced
Virtual whiteboard and shared document workspace