Advanced GenAI Tools Certification Series
Monitoring, Reliability & Change Management
Workshop
Do your GenAI toolchains behave reliably when you ship new models, tools, and prompts?
Monitoring and change management are foundational for any production GenAI platform, but toolchains can become opaque, brittle, and risky as they scale across services, APIs, and environments.
To win, your GenAI solutions need observable, SLO-driven toolchains with safe, repeatable rollout patterns.
The Challenge
Without a strong approach to monitoring, reliability, and change management, teams struggle to:
- See end-to-end behavior — Gaps in tracing and logging make it hard to understand what is happening across tools, models, and APIs.
- Control rollout risk — Every configuration, model, or tool update feels risky without canaries, experiments, or clear rollback paths.
- Resolve incidents quickly — Weak SLOs and fragmented metrics slow diagnosis, extend downtime, and erode stakeholder trust.
Monitoring and change-management gaps will drive more incidents, slower recovery, and declining confidence in your GenAI platform.
Our Solution
In this hands-on workshop, your team instruments end-to-end GenAI toolchains with tracing, metrics, SLOs, and controlled rollout strategies using curated labs and prebuilt flows. Areas of focus include:
- End-to-end Toolchain Tracing — Implement tracing across tools so every request path is visible for diagnostics and debugging.
- Defining and Monitoring SLOs — Set and track latency, availability, and error SLOs that match real user expectations.
- Controlled Rollouts & Experiments — Apply canaries and A/B tests to validate toolchain changes before full release.
- Interactive Labs & Observability Stack — Work inside Jupyter or IDE-based labs wired to a monitoring and tracing stack.
- Capstone & Live Coaching — Design and review a monitoring and rollout plan for a realistic GenAI toolchain change.
Skills You'll Gain
- Production Observability — Design tracing, logging, and metrics that make complex GenAI toolchains understandable.
- SLO-Driven Reliability — Define, monitor, and act on SLOs and error budgets that align to critical user journeys.
- Safer Change Management — Plan and execute toolchain updates using canaries, A/B tests, and clear rollback strategies.
- Faster Incident Response — Use metrics, logs, and traces to pinpoint issues quickly and restore service faster.
- Reusable Monitoring Patterns — Create templates for monitoring and rollout you can reuse across future GenAI workflows.
Who Should Attend:
Solution Essentials
Format
Virtual or in-person
Duration
4 Hours
Skill Level
Comfortable with basic Python and production or cloud tooling recommended
Tools
Jupyter or IDE-based labs plus a preconfigured monitoring and tracing stack
Explore the Remaining Advanced GenAI Tools Certification Workshops
Help your teams master advanced GenAI Tool concepts and solutions. Click below to explore the remaining workshops in the Advanced GenAI Tools certification series.
Orchestration & Control
Explainability & Customization
MCP & Model + Tool
Co-Processing
Co-Processing
Self-Tuning / Adaptive Tool Invocation
Tool Cost & Resource Optimization