Defining Your LLM EaaS Vision & Strategy
A Model EaaS vision is only credible if the enterprise can produce decision-grade evaluation evidence. This workshop focuses on the data and workflow foundations required to make evaluation repeatable, comparable, and trusted at scale.
Leave with a clear Model EaaS strategy and a plan to build the evaluation datasets and pipelines needed to scale it.
Many organizations want consistent model evaluation, but underestimate the dataset and pipeline work required to make EaaS repeatable at enterprise scale.
- Evaluation datasets aren’t representative or trusted: Test data is incomplete, biased toward what’s easy to collect, or inconsistently annotated—making results hard to rely on.
- Data preparation is manual and doesn’t scale: Normalization, feature engineering, and dataset creation are repeated for each initiative, slowing evaluation and increasing inconsistency.
- Lineage and reuse are weak: Teams can’t easily explain what was evaluated, what changed, or reuse datasets across domains—undermining comparability over time.
Without durable datasets and pipelines, Model EaaS becomes a vision—without the operational capability to deliver it.
We help teams define a Model EaaS strategy that’s grounded in the real requirements of scalable evaluation datasets and data operations.
- Define the requirements for quality evaluation datasets: Establish what “good” looks like for coverage, representativeness, and consistency so evaluation results are decision-grade.
- Design sourcing and annotation for diverse test data: Create an approach to collect and label test data that reflects real enterprise variation, not just ideal scenarios.
- Standardize normalization and feature engineering practices: Identify the preparation steps needed to make datasets comparable and reusable across models and use cases.
- Create reusable datasets with strong lineage tracking: Define how datasets are versioned, governed, and traced so results remain explainable and defensible over time.
- Automate data pipelines to support scalable evaluations: Map the pipeline capabilities needed to refresh datasets, run evaluations repeatedly, and keep EaaS current as the enterprise evolves.
- Requirements for quality evaluation datasets
- Sourcing diverse, representative test data
- Annotating evaluation data consistently
- Data normalization to support comparability
- Feature engineering for evaluation datasets
- Creating reusable datasets with strong lineage tracking
- Automating data pipelines to support scalable evaluations
- Define what “decision-grade” evaluation datasets must include for your enterprise and priority use cases
- Identify the key gaps in sourcing, annotation, normalization, and reuse that prevent scalable evaluation today
- Establish principles for dataset consistency, versioning, and lineage so results remain comparable over time
- Outline the automation and pipeline capabilities needed to make evaluation repeatable and sustainable
- Leave with a practical strategy and roadmap to build the dataset foundation for Model EaaS
Who Should Attend:
Solution Essentials
Facilitated workshop (interactive discussion + working session)
8 hours
Advanced
Virtual whiteboard and shared document workspace