Role-Based Programme
RB0933

Hugging Face for Product & Service Management

Evaluate Models, Prototype AI Features & Build Responsible AI Products

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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 discover and evaluate AI models, datasets, and capabilities for product and service use cases.
  • Translate business and customer requirements into appropriate AI model, data, inference, and prototype requirements.
  • Compare candidate models using documentation, evaluation results, limitations, licensing, performance, and product-fit criteria.
  • Build and validate AI-powered product concepts using Hugging Face Spaces and inference capabilities.
  • Develop reusable product-management workflows covering discovery, feasibility, prototyping, evaluation, governance, and deployment decisions.

Tools covered

Hugging Face HubModel HubDataset HubModel CardsDataset CardsSpacesInference ProvidersInference EndpointsModel EvaluationRepository Metadata

Who should attend

  • Product Managers
  • AI Product Managers
  • Product Owners
  • Service Managers
  • Product Operations Professionals
  • Product Analysts
  • Business Analysts
  • Product Strategy Professionals
  • Technical Product Managers
  • Innovation Managers
  • Digital Product Professionals
  • Solution & Product Consultants
  • Product & Service Management Leaders

Prerequisites & Participant Readiness

  • Basic understanding of product or service management
  • Basic awareness of Artificial Intelligence and Machine Learning concepts
  • Familiarity with product requirements, customer problems, and feature prioritisation
  • Basic understanding of APIs or software products is helpful
  • No advanced programming or model-development expertise required
  • No previous Hugging Face experience required

TOC Modules

Concepts
  • Understanding the Hugging Face ecosystem and the role of the Hub
  • Understanding models, datasets, Spaces, repositories, and inference services
  • Identifying AI product opportunities across text, vision, speech, and multimodal use cases
  • Understanding the AI Product Need → Model → Data → Prototype → Evaluation → Deployment lifecycle
Practical activities
  • Exploring the Hugging Face Hub from a product-manager perspective
  • Identifying models and datasets relevant to a sample product requirement
  • Mapping a customer problem to potential AI capabilities

Scenarios

Customer Need to AI Feature Prototype

Customer Problem → AI Capability Mapping → Hugging Face Model Discovery → Model & Dataset Review → Evaluation → Space Prototype → User Feedback → Product Recommendation

Participants take a customer problem through an end-to-end AI product discovery process, selecting suitable models, validating their capabilities, and developing a prototype-backed recommendation for stakeholder review.

AI Model Selection for a Production Service

Product Requirement → Candidate Models → Model Cards → Evaluation Results → Product Testing → Inference Options → Risk Assessment → Deployment Recommendation

Participants compare multiple Hugging Face models for a production use case and prepare an evidence-based recommendation covering product fit, performance, limitations, deployment requirements, and governance considerations.

**Current Capability Reference:** Hugging Face Model Cards can document intended use, limitations, training datasets, and evaluation results; Dataset Cards provide dataset context and metadata; Spaces support rapid ML application demos; and Inference Providers and Inference Endpoints support model experimentation and managed deployment workflows.

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