Building Practical AI Evaluation Workflows

In this session, we’ll show how to make open-ended AI outputs quantifiable, turning evaluation into clear, repeatable metrics tied to your business outcome

Join us to learn how teams across industries are building reliable, compliant, and explainable AI evaluation frameworks using Label Studio; and why this shift is essential for scaling AI responsibly.

You’ll walk away understanding:

• Why benchmarks exist — and what happens when they’re missing.
• How to capture human subject matter expertise in an evaluation framework that captures nuanced quality dimensions.
• How to benchmark AI performance in expert-driven domains — using LLMs responsibly as evaluators to scale human judgment
• What global governance frameworks require (SR 11-7, NIST AI RMF, EU AI Act).

This session is designed for AI product, platform, and data science leaders who want to make model evaluation objective, auditable, and actionable.

What You’ll Learn

1. The Role of Benchmarks in Reliable AI

Why benchmarks are foundational to evaluating model risk and quality — and how they provide repeatable, interpretable structure to AI evaluation.

2. From SME expertise → Rubrics → Results

How to define rubrics that capture human expectations and tie evaluation metrics to real business outcomes.

3. Benchmarks in Action

See how teams are using Label Studio to evaluate model reasoning in specialized domains, including a case study of a legal benchmark built with industry experts.

4. Regulatory Readiness

Learn how global frameworks like the EU AI Act, NIST AI RMF, and SR 11-7 shape expectations for measurable AI performance — and how benchmarks help teams mitigate risk.

Reserve Your Spot

This live session is free and open to the community, but space is limited. Reserve your seat today and get early access to the companion workshop and resource bundle.

And you’ll get early access to register for the hands-on workshop: 👉 Part 2: Building Rubric-Based Benchmarks in Label Studio (Dec 12, 2025)