AIDU-EVAL-202
Delivery Type: Live, instructor-led Remote or In person
Prerequisite: AI Safety
This course provides a structured, non-technical approach to evaluating AI systems and AI vendors in real organizational settings. It explains why AI evaluation is inherently complex, why benchmarks, pilots, and demos often mislead, and why performance, safety, risk, and ROI must be assessed at the system level rather than the model level.
Participants learn how to design meaningful evaluation and stress-testing strategies, monitor deployed systems over time, and critically assess vendor claims and due diligence factors. The course emphasizes lifecycle-aware evaluation, recognizing that many AI initiatives fail after rollout due to drift, hidden costs, governance gaps, and misaligned incentives.
The course concludes with decision frameworks for determining when to deploy, limit, or reject AI systems. It is designed for professionals responsible for approving, governing, or overseeing AI initiatives, without requiring coding or mathematical knowledge.
Core Topics:
AI evaluation complexity and scope
Model-level versus system-level evaluation
Benchmarking and metrics reality
Monitoring, drift, and lifecycle degradation
Test design and stress-testing principles
AI performance evaluation frameworks
AI safety and risk assessment frameworks
AI vendor evaluation and due diligence
AI initiative ROI analysis frameworks
Final deployment decision principles
Outcomes:
Evaluate AI systems beyond accuracy, demos, and benchmark claims
Distinguish model-level performance from system-level behavior and risk
Identify safety, robustness, bias, and misuse risks
Critically assess AI vendor claims and marketing language
Recognize evaluation failures that cause post-deployment collapse
Design lifecycle-aware evaluation and monitoring strategies
Measure business impact and ROI realistically
Make defensible procurement and governance decisions
