Role-Based Programme
RB0925

Hugging Face for Quality Management

AI Model Validation, Quality Assurance & Continuous Improvement

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Duration
16 Hours
Level
Intermediate
Delivery
Instructor-Led
Format
Capability Training

Programme Objectives

  • Develop functional proficiency in using Hugging Face to review and assess AI models, datasets, documentation, and evaluation evidence from a quality-management perspective.
  • Apply structured quality criteria to model performance, data quality, reliability, limitations, bias, security, and intended-use requirements.
  • Build repeatable workflows for model evaluation, comparative testing, quality reviews, non-conformity identification, and improvement tracking.
  • Use Model Cards, Dataset Cards, evaluation metrics, and repository information to strengthen AI quality documentation and traceability.
  • Apply quality assurance, change-control, risk-based thinking, and human validation throughout the AI model lifecycle.

Tools covered

Hugging Face HubModel CardsDataset CardsModel & Dataset RepositoriesHugging Face EvaluateEvaluation MetricsModel ComparisonRepository Security ScanningModel Versioning

Who should attend

  • Quality Managers
  • Quality Assurance Professionals
  • Quality Engineers
  • Quality Control Professionals
  • AI Quality & Validation Professionals
  • Software Quality Assurance Professionals
  • Model Validation Professionals
  • Quality Systems Professionals
  • Process Excellence Professionals
  • Continuous Improvement Professionals
  • Quality Auditors
  • Technology Quality Professionals
  • AI Governance Professionals
  • Quality Team Leads

Prerequisites & Participant Readiness

  • Basic understanding of quality assurance, validation, or quality-management activities
  • Basic awareness of Artificial Intelligence and Machine Learning concepts is helpful
  • Familiarity with testing, quality criteria, or process controls is beneficial
  • Basic understanding of datasets and performance metrics is helpful
  • No advanced programming or model-development expertise required
  • No previous Hugging Face experience required

TOC Modules

Concepts
  • Understanding Hugging Face and the role of the Hugging Face Hub in the AI lifecycle
  • Understanding models, datasets, repositories, versions, and AI artifacts
  • Identifying quality considerations across data, model, evaluation, deployment, and change
  • Understanding the difference between model capability and validated fitness for use
Practical activities
  • Navigating Hugging Face models and datasets from a quality perspective
  • Reviewing a sample AI asset against basic quality criteria
  • Creating an initial AI Quality Review checklist

Scenarios

AI Model Quality Validation Before Release

Business Requirement → Candidate Model → Model & Dataset Review → Acceptance Criteria → Evaluation → Error Analysis → Quality Risks → Release Recommendation

Participants perform a structured quality assessment of a Hugging Face model, evaluate its evidence against predefined requirements, identify quality gaps, and prepare a release-readiness recommendation.

Model Update & Quality Revalidation

Existing Model → New Version → Change Review → Regression Test Cases → Performance Comparison → Non-Conformities → Corrective Action → Revalidation Decision

Participants assess an updated model version, identify potential quality impacts, conduct structured regression review, and determine whether the updated model satisfies defined quality requirements.

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