concept#Machine Learning#Quality Assurance#Data#Observability

Model Validation

Model validation comprises practices and criteria to evaluate machine learning models, ensuring robustness, generalization and fairness. It defines tests, metrics and acceptance criteria across training and production stages.

Model validation describes practices for evaluating and assuring machine learning models using tests, metrics and data checks.

Maturity

Established

Cognitive loadMedium

360° overview

Model Validation

Six perspectives place the building block in context. The numbers show where each perspective continues in the reading path.

The building block at a glance

Model Validation

360°

Classification

2

Complexity: Medium
Impact area: Technical

Technical context

2

Integrations

TensorFlow Data Validation (TFDV)

+2

Principles & goals

1

Early and repeatable tests across the ML lifecycle

Value stream stage: Build

Use cases & scenarios

4

Use cases: 3
Scenarios: 3

Compromises

3

Risks: 3
Best practices: 3

I/O & resources

4

Inputs: 3
Outputs: 3