- Risk and compliance teams
- AI product owners
- Engineering leaders
- Regulated-industry adopters
Catalogue › Data & Artificial Intelligence › Model Testing as a Service
Data & Artificial Intelligence
Model Testing as a Service
Independent testing of machine-learning models before and after deployment: accuracy against holdout data, robustness under drift and adversarial input, bias and fairness measurement, and behavioural verification of generative systems - reported as evidence a decision-maker can act on.
- Model weaknesses known before production finds them
- Bias and robustness measured, not assumed
- A documented basis for go or no-go decisions
- Ongoing assurance as data drifts
- Holdout and cross-validation testing
- Robustness, drift and stress evaluation
- Bias and fairness measurement
- Generative-output behavioural testing
- Adversarial probing within agreed scope
- Production monitoring handover
- Agreed test plan and acceptance thresholds
- Independent evaluation report with findings
- Bias, robustness and drift measurements
- Reproducible test harnesses
- Remediation recommendations with priorities
- Pre-deployment certification cycle
- Recurring assurance cycles
- Generative-AI behavioural testing
- Regulatory-evidence packs
- Model count and class
- Test depth and threshold strictness
- Data availability for testing
- Regulatory evidence requirements
- Cycle frequency
Content on this page comes from the governed ARRIX catalogue record DAI-11; pricing is confirmed only through a reviewed quotation.