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Also known as: Model Verification, Correctness Check
The process of confirming a system or model correctly implements its specified requirements.
DESCRIPTION
Verification asks, “Did we build the system right?” by checking conformance to specifications through static analysis, reviews, and testing. It complements validation, which asks, “Did we build the right system?” Formal methods and model checking can mathematically prove properties for critical systems. In data and ML, verification includes schema checks, unit tests, and pipeline assertions. Effective verification reduces defects, improves reliability, and lowers downstream costs. It requires clear, testable requirements and traceability.
ALTERNATIVE DEFINITIONS
NIST CSRC
Confirmation, through the provision of objective evidence, that specified requirements have been fulfilled.
Concise, requirement-focused definition used across standards.
ISO 9000
confirmation, through the provision of objective evidence, that specified requirements have been fulfilled
Canonical quality management definition mirrored in many disciplines.
RELATED TERMS
Testing Quality Assurance Static Analysis Unit Testing Formal Verification Model Checking Acceptance Testing Specification Verification and Validation
COMPETING TERMS
RELATIONS
- Narrower: Formal Verification
KNOWLEDGE AREAS
Testing & Validation Software Engineering MLOps Data Quality
TARGET AUDIENCE
Software Developer DevOps Engineer MLOps Engineer Data Engineer Data Scientist Data Steward
EXTERNAL SOURCES
- Wikipedia - Verification and validation
- NIST CSRC - Authoritative glossary definition
- ISO - ISO 9000 vocabulary for quality
TOP QUESTIONS
How does Verification differ from Validation?
Verification checks conformance to specified requirements; validation checks fitness for intended use. Put simply: build the system right vs. build the right system.
Category: Misunderstanding
Why is Verification essential in MLOps?
It ensures data schemas, features, and pipelines conform to design, preventing silent errors before deployment and reducing model performance regressions.
Category: Relevancy
What are common Verification techniques?
Code reviews, static analysis, unit and integration tests, assertions, and formal proofs for critical properties are common techniques.
Category: Basics
What is the biggest mistake in Verification?
Vague requirements. Without precise, testable specifications, verification is ineffective and leaves gaps that surface in production.
Category: Mistakes
How is Verification applied to data pipelines?
Schema validation, data contract checks, expectations on ranges and nulls, and lineage-based impact tests ensure correctness over time.
Category: Application
What is formal Verification and when to use it?
It mathematically proves properties (e.g., safety, liveness). Use for high-assurance systems like avionics, medical devices, and cryptography.
Category: Requirements
What risks arise from skipping Verification?
Defects reach production, causing outages, data corruption, or compliance failures. Costs escalate the later defects are found.
Category: Risks
How does Verification relate to acceptance testing?
Verification feeds into acceptance by proving components meet requirements, enabling confident end-to-end validation with stakeholders.
Category: BestPractices
What artifacts support effective Verification?
Traceable requirements, test plans, automated test suites, coverage metrics, and documented review outcomes provide evidence.
Category: Requirements