Role-Based Programme
RB0924

Hugging Face for Quality Management

Evaluate AI Models, Data Quality & Performance with Confidence

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Duration
8 Hours
Level
Basic
Delivery
Instructor-Led
Format
Workshop

Programme Objectives

  • Build foundational capability to use Hugging Face for reviewing AI models, datasets, documentation, and evaluation evidence.
  • Assess model quality using intended-use information, limitations, evaluation metrics, benchmark results, and documented testing evidence.
  • Examine dataset quality, provenance, bias considerations, licensing, and suitability for defined business requirements.
  • Compare candidate AI models against structured quality and acceptance criteria.
  • Develop repeatable quality-review workflows for AI model selection, validation, release readiness, and continuous improvement.

Tools covered

Hugging Face HubModel CardsDataset CardsModel RepositoriesEvaluation ResultsCommunity LeaderboardsRepository Metadata

Who should attend

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

Prerequisites & Participant Readiness

  • Basic understanding of quality assurance or quality-management concepts
  • Familiarity with testing, validation, acceptance criteria, or quality reviews is helpful
  • Basic awareness of Artificial Intelligence and Machine Learning is beneficial
  • No advanced data-science or programming knowledge required
  • No previous Hugging Face experience required

TOC Modules

Concepts
  • Understanding the Hugging Face Hub and its models, datasets, repositories, and AI resources
  • Understanding AI quality dimensions such as accuracy, reliability, consistency, suitability, and limitations
  • Identifying where quality teams contribute across AI selection, testing, validation, and release
  • Understanding the Model → Data → Evaluation → Validation → Approval lifecycle
Practical activities
  • Navigating the Hugging Face Hub from a quality-review perspective
  • Selecting a sample AI model and identifying its available quality information

Scenarios

AI Model Quality Evaluation Before Adoption

Business Requirement → Hugging Face Model Search → Model Card → Dataset Review → Evaluation Metrics → Quality Scorecard → Validation → Recommendation

Participants evaluate candidate Hugging Face models against defined quality requirements and prepare a structured recommendation identifying strengths, limitations, evidence gaps, and further testing requirements.

AI Quality Issue & Continuous Improvement Review

Model Performance Issue → Documentation & Evaluation Review → Dataset Investigation → Quality Gap → Additional Testing → Corrective Actions → Revalidation

Participants investigate an AI quality concern using Hugging Face model and dataset information, identify potential quality gaps, define additional validation requirements, and prepare corrective-action recommendations.

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