Even the best evaluation framework breaks down if the underlying data is incomplete, biased, or inconsistent. This workshop focuses on preparing high-quality evaluation data so model comparisons are fair, reliable, and reproducible.
To win, teams need evaluation data that accurately represents real tasks, real scenarios, and real constraints.
Teams preparing data for model evaluation commonly encounter:
- Irrelevant or incomplete datasets: Use examples that don’t reflect real tasks or miss critical edge cases.
- Hidden bias and coverage gaps: Evaluate models on data that underrepresents key scenarios or stakeholders.
- Unreliable comparisons: Change datasets or inputs between runs, making results hard to trust or reproduce.
Weak evaluation data leads to misleading results and poor model selection decisions.
In this hands-on workshop, your team prepares robust, representative evaluation data and benchmarks to support fair and repeatable model comparisons.
- Identify datasets and examples that reflect real evaluation needs.
- Assess data coverage and representation across key scenarios.
- Create benchmark tasks aligned to target model use cases.
- Clean and prepare evaluation inputs for consistency.
- Establish version control practices for reliable, repeatable evaluations.
- Identifying Relevant Evaluation Datasets
- Assessing Data Coverage and Representation
- Creating Benchmarks for Target Tasks
- Cleaning and Preparing Evaluation Inputs
- Maintaining Data Version Control
- Select evaluation data that reflects real-world model usage.
- Identify gaps and bias in evaluation datasets before testing.
- Build benchmarks that fairly compare model performance.
- Standardize and clean inputs for consistent evaluation runs.
- Maintain versioned evaluation data for reproducible results.
Who Should Attend:
Solution Essentials
Virtual or in-person
4 hours
Intermediate; familiarity with data preparation and GenAI concepts recommended
Data preparation templates, benchmark examples, and versioning workflows