Specialist

AI for Performance, Reliability and Resilience Engineering

Specialist depth for using AI across workload analysis, capacity, performance testing, diagnosis, observability, failure analysis, resilience validation, and incident learning.

The workplace problem

Why this learning matters

Performance and reliability teams need AI assistance that accelerates investigation and design without replacing evidence, systems reasoning, controlled experiments, or accountable operational decisions.

Intended outcomes

What participants will learn to do

  • Apply AI assistance to bounded performance, reliability, and resilience activities with explicit evidence requirements.
  • Improve workload analysis, diagnosis, test design, observability, and failure learning without substituting generated explanations for verification.
  • Retain accountable operational decisions, safe experiments, and escalation during incidents.

Application at work

How participants can apply it at work

  • An AI-assisted reliability workflow with evidence, experiment, and escalation boundaries.
  • A reviewed performance, capacity, failure-analysis, or resilience-validation artifact.

Shaped to context

Shaped around your context

Flow Cracker shapes this specialist workshop around the customer's system architecture, workloads, service expectations, observability, incident practices, failure modes, and operating constraints.

Entry context: Participants should bring practical experience in distributed systems, observability, cloud or platform operation, performance testing, capacity, incidents, or resilience engineering.

Who it is for

Who this is for

  • Performance, site-reliability, resilience, platform, cloud, and DevOps engineers
  • Software engineers, solution architects, capacity planners, and operations engineers
  • Technical leads and managers accountable for dependable production systems

Opportunity lenses

  • AI to build the product

Capabilities strengthened

  • Engineering, Architecture & Verification

Connected learning

Discuss the context

Shape the learning around the work that needs to change

Share the audience, workplace problem, constraints, and evidence you want the learning to address.

Discuss this learning need