Model Training & Data

Data and model lifecycle practices used to produce repeatable training, evaluation, and versioned releases.

Data sources and preparation

Define source systems, ownership, and data access rules.
Validate inputs, handle missing values, and document assumptions.
Maintain dataset versions with reproducible transforms.

Training and tuning

Train models with controlled configurations and repeatable runs.
Track experiments, hyperparameters, and evaluation metrics.
Document limitations, intended use, and boundary conditions.

Evaluation

Use fixed test sets and time-sliced validation where appropriate.
Measure accuracy/quality plus stability and error distribution.
Include fairness/bias checks when required by the use case.

Release management

Version models, datasets, prompts, and configuration together.
Support rollback and change approval workflows.
Maintain an audit trail for training data and model artifacts.

Artifacts produced

Dataset and transform documentation (lineage, assumptions, versions).
Evaluation reports and experiment tracking outputs.
Release records (versions, approvals, change notes).

Connected pillars: Explainable AI; Security & Privacy
Applied pattern: AI Personal Model

AI Personal Model Explainable AI Security & Privacy

CTA

Contact Maloni to discuss requirements, constraints, and next steps.