~/talk

Ensuring Reliable Evaluation Systems for Your ML

Talkat swampUP 2025 · 2025-09-08..09-10· [machine-learning]
links:none on file
slides:none on file

Concrete, actionable steps for turning vague benchmarks into trustworthy evaluation: define a contract-aligned evaluation suite tied to specific user intents, assemble a golden dataset of targeted edge cases and stratified slices, enforce inter-annotator agreement thresholds and audit label drift, instrument continuous monitoring and scheduled relabeling, and plug evaluation into the CI/CD pipeline so metrics gate promotions with clear pass/fail thresholds and automated slice analysis.

AI-written summary · about

  1. 0:00Introduction and conference praise
  2. 2:28Agentification of software explained
  3. 9:21UX challenges with current agents
  4. 18:33Beyond chat interfaces for agents
  5. 24:59Gateway of the internet battle
  6. 25:45Trust issues in agentic systems
  7. 32:51Potential future of agentic marketplaces
  8. 34:40Closing remarks and Q&A
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