04 / AI
Datasets & evaluation
Measurable intelligence, in the real world
Prepare and version data, separate tests and compare metrics to make results, limits and experiment evolution understandable.
Platforms and tools follow your context. Some examples:
AI
Datasets & evaluation
Organise data and compare results
Collection & sources
Real data · synthetic dataKnown origin and conditions
Labels & structure
Classes · metadata · formatsData criteria and quality
Data separation
Training · validation · testingChecks on separate sets
Model comparison
Baselines · experiments · parametersRepeatable comparisons
Metrics & evidence
Errors · coverage · resultsUnderstanding the limits
Versions & traceability
Datasets · models · configurationsReproducible results
The technologies shown are examples.
Engineering support within your team, with shared tools and working methods.
TECHNICAL SCOPE
Datasets, synthetic data and AI model evaluation
A dataset must represent the context in which the model will be used. Provenance, annotations, imbalance and possible leakage between splits are examined. Synthetic data explore controlled conditions or variations; their contribution is measured using a relevant separate test. Each experiment retains configuration and data references.
Activities and verification
- Inventory, labelling criteria and sample quality checks.
- Split by source, time or subject where needed for valid evaluation.
- Compare baselines, per-class metrics and errors under critical conditions.
Initial decisions and integration
I review data origins, usage rights, annotations and conditions with the team. I can contribute to cleaning, splitting, versioning or experiment comparisons while retaining client environments and confidentiality rules. Metrics follow the decision being supported.
A verification example
Duplicates and correlations between sets are checked: frames from one video or samples from one object may require group-based splitting. Training, validation and final testing have distinct roles; the test is not used to select models and parameters. Results and sample distributions remain documented.
Deliverables and acceptance criteria
Organised dataset, split rules, configurations and experiment reports. Results include sample counts, limitations and observed variability rather than a single accuracy figure.
Where to start
Bring usable samples, capture and annotation criteria, confidentiality constraints and expected operating conditions.
Does high accuracy prove a model is ready?
It must be read alongside data representativeness, classes, error costs and operating conditions. A single value does not describe all performance needed for a project.
THE NEXT STEP