Evaluation dataset
Separate development and holdout examples, document provenance and include edge cases relevant to the deployment.
AI/ML Model Testing
AI/ML Model Testing
Evaluate whether an AI or ML model is suitable for a defined task before it becomes part of a production decision.
Inside the delivery
Evaluate model behavior before a release decision
Define the task, failure severity and the population the test data must represent. The flow below shows the main delivery stages and the evidence produced at each step.
- 01
Evaluation design
Define the task, failure severity and the population the test data must represent.
Output
Evaluation protocol and acceptance thresholds
- 02
Dataset preparation
Separate development and holdout examples; review labels, leakage and missing cohorts.
Output
Versioned evaluation dataset
- 03
Behavior testing
Measure task performance, robustness and error patterns across relevant slices and adversarial inputs.
Output
Reproducible test runs and error analysis
- 04
Release assessment
Document limitations, failed criteria and the conditions under which the model should be retested.
Output
Release evaluation report and regression suite
Controls across the workflow
- Holdout separation
- Dataset versioning
- Slice-level results
- Reproducible runs
Decisions that shape the scope
- Does a passing evaluation prove a model is safe?
- It supports a decision for the tested task, data and version. It cannot establish all future behavior; new inputs, model updates and changing populations require further evaluation.
- What needs to be available before delivery?
- Model access, intended task, representative examples, labeling guidance, failure priorities and the planned deployment context.
Before you commit
Is this the right engagement?
- What we need from you
- Model access, intended-use boundaries, representative labelled examples, known failure cases and acceptable error costs.
- How you accept the work
- Report results by important data slice, document test-set limitations and compare with an agreed baseline. Repeat the suite on a candidate model update.
- Scope & alternatives
- It supports a decision for the tested task, data and version. It cannot establish all future behavior; new inputs, model updates and changing populations require further evaluation.
Capabilities
Engineering scope and deliverables
Select the work that addresses your constraint. Responsibilities and acceptance criteria are agreed before delivery.
Behavior and robustness tests
Measure errors across input groups and changed conditions, with model-specific checks for grounding or classification quality.
Model release report
Record the model version, configuration, evaluation results and limitations so release decisions can be reproduced.
Integrations
Selected for your environment
Tools are chosen around your existing systems, access requirements and operating constraints.
Web and mobile applications
APIs and services
CI/CD pipelines
Cloud test environments
Observability tools
Test management systems
Frequently asked questions
Questions to resolve before starting
01Does a passing evaluation prove a model is safe?
Does a passing evaluation prove a model is safe?
It supports a decision for the tested task, data and version. It cannot establish all future behavior; new inputs, model updates and changing populations require further evaluation.
02What should we prepare for the first technical discussion?
What should we prepare for the first technical discussion?
Model access, intended task, representative examples, labeling guidance, failure priorities and the planned deployment context.
03What evidence is available at handover?
What evidence is available at handover?
The agreed delivery includes release evaluation report and regression suite. Document limitations, failed criteria and the conditions under which the model should be retested.
04How is the engagement estimated?
How is the engagement estimated?
We review the available inputs before estimating: Model access, intended task, representative examples, labeling guidance, failure priorities and the planned deployment context. The proposal identifies dependencies, review milestones and excluded work; the scope determines the schedule.
Start a conversation
Discuss your next technical step
Share your current situation and the constraint you need to resolve. We will use the discovery inputs above to define a practical scope for AI/ML Model Testing.