Role-Based Programme
RB0922

Hugging Face for Information Technology

Architect, Deploy, Secure & Govern Enterprise AI Infrastructure

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Duration
32 Hours
Level
Advanced
Delivery
Instructor-Led
Format
Capability Development

Programme Objectives

  • Develop advanced expertise in managing Hugging Face models, datasets, repositories, inference services, and AI application infrastructure within enterprise IT environments.
  • Architect and integrate production-ready AI services using Hugging Face APIs, Inference Providers, Spaces, and dedicated Inference Endpoints.
  • Implement security, access control, private networking, repository governance, artifact validation, and enterprise AI-resource management.
  • Optimise AI workloads for performance, scalability, reliability, observability, and operational efficiency.
  • Build governed and reusable enterprise AI platform workflows covering model onboarding, deployment, automation, monitoring, lifecycle management, and incident response.

Tools covered

Hugging Face HubTransformersModel & Dataset RepositoriesModel CardsDataset CardsHugging Face CLI & APIsInference PlaygroundInference ProvidersSpacesInference EndpointsPrivate EndpointsAutoscalingSafetensorsSecurity ScanningAccess TokensOrganizationsResource GroupsAudit LogsJobs & Scheduled Jobs

Who should attend

  • IT Architects
  • Solution Architects
  • Cloud Engineers
  • DevOps Engineers
  • Platform Engineers
  • Infrastructure Engineers
  • AI Platform Engineers
  • Application Developers
  • Application Support Engineers
  • System Administrators
  • Site Reliability Engineers
  • Integration Engineers
  • Information Security Engineers
  • IT Operations Managers
  • Technical Leads & Engineering Managers

Prerequisites & Participant Readiness

  • Working knowledge of enterprise IT infrastructure and application architecture
  • Basic familiarity with Artificial Intelligence and Machine Learning concepts
  • Familiarity with REST APIs, authentication, and application integration
  • Basic Python knowledge is recommended for practical exercises
  • Familiarity with Git, repositories, command-line tools, and cloud environments is beneficial
  • Basic understanding of containers, networking, and security concepts is helpful
  • Previous Hugging Face experience is beneficial but not mandatory
  • Completion of shorter Hugging Face programmes is not required

TOC Modules

Concepts
  • Understanding the Hugging Face ecosystem across Hub, models, datasets, Spaces, inference, and enterprise services
  • Understanding the Model → Repository → Inference → Application → Operations lifecycle
  • Mapping Hugging Face components to enterprise IT architecture
  • Understanding public, private, and organisation-managed AI resources
Practical activities
  • Navigating the Hugging Face Hub from an enterprise IT perspective
  • Mapping a sample enterprise AI requirement to suitable Hugging Face components
  • Designing a high-level Hugging Face solution architecture

Scenarios

Enterprise AI Model Service Deployment

Business Application → Model Discovery → Technical Evaluation → Private Repository → Security Validation → Inference Endpoint → Private Access → Autoscaling → Monitoring → Production Operations

Participants design a production-ready AI service using Hugging Face, covering model onboarding, repository controls, secure deployment, capacity planning, network architecture, monitoring, and operational support.

Governed Enterprise AI Platform

Multiple IT Teams → Hugging Face Organization → Resource Groups → Roles & Tokens → Approved Repositories → Deployment Services → Audit Logs → Lifecycle Governance

Participants architect a governed Hugging Face environment for multiple technology teams, implementing least-privilege access, controlled AI assets, deployment governance, traceability, and repeatable operational workflows.

## Current Capability Reference

Hugging Face currently supports enterprise **Resource Groups** with fine-grained roles and controls across repositories and services, while **Audit Logs** record administrative and resource-level activity for operational and governance review.

Dedicated **Inference Endpoints** support public, protected, and private deployment configurations, TLS-protected traffic, private connectivity options, and autoscaling based on utilisation or pending requests.

Hugging Face also provides repository security scanning for potentially unsafe model artifacts, while **Inference Providers** offers a unified API layer across multiple model-serving providers and model types.

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