Role-Based Programme
RB0921

Hugging Face for Information Technology

Deploy, Secure & Operate Enterprise AI Solutions

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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, evaluate, manage, deploy, and operate AI models within IT environments.
  • Build reusable workflows for model repositories, inference services, authentication, access control, security, monitoring, and troubleshooting.
  • Configure and evaluate AI deployment environments using appropriate endpoint, scaling, networking, and operational controls.
  • Apply security and governance practices to third-party models, repositories, credentials, organizational access, and production AI services.
  • Integrate Hugging Face capabilities into structured IT operations while maintaining reliability, traceability, security, and human oversight.

Tools covered

Hugging Face HubModel RepositoriesModel CardsDatasetsSpacesInference EndpointsOrganizations & Access ControlResource GroupsUser Access TokensSecurity ScanningRuntime LogsAnalytics & Autoscaling

Who should attend

  • IT Operations Professionals
  • System Administrators
  • Cloud Engineers
  • DevOps Engineers
  • Platform Engineers
  • Application Support Professionals
  • AI / ML Platform Engineers
  • Infrastructure Engineers
  • Solution Architects
  • Integration Engineers
  • Site Reliability Engineers
  • IT Security Professionals
  • Technical Leads
  • IT Infrastructure & Platform Managers

Prerequisites & Participant Readiness

  • Basic understanding of IT infrastructure, applications, or cloud environments
  • Familiarity with APIs, authentication, networking, or deployment concepts
  • Basic understanding of AI and Machine Learning concepts is helpful
  • Familiarity with command-line or scripting environments is beneficial
  • No advanced model-development expertise required
  • No previous Hugging Face experience required

TOC Modules

Concepts
  • Understanding Hugging Face Hub and its role within enterprise AI infrastructure
  • Understanding models, datasets, Spaces, repositories, and inference services
  • Understanding the AI lifecycle from model discovery to operational deployment
  • Identifying responsibilities across IT, development, security, and AI teams
Practical activities
  • Navigating the Hugging Face Hub from an IT operations perspective
  • Reviewing a model repository, associated files, metadata, and documentation
  • Mapping a Model → Repository → Deployment → Application workflow

Scenarios

Enterprise AI Model to Secure Production Service

Business Requirement → Model Discovery → Technical Evaluation → Repository Review → Security Check → Access Control → Inference Endpoint → Testing → Monitoring → Production Readiness

Participants take a selected Hugging Face model through a structured IT deployment workflow, applying security, access, endpoint, performance, and operational-readiness controls before production use.

Production AI Service Incident & Recovery

Application Failure → Endpoint Status → Runtime Logs → Analytics → Root-Cause Investigation → Configuration / Scaling Action → Validation → Recovery → Incident Documentation

Participants investigate a simulated production AI service issue using endpoint logs and operational metrics, implement an appropriate corrective action, validate service recovery, and document the incident for future operational improvement.

*Current Hugging Face capabilities reflected in this course include organization roles and Resource Groups, fine-grained access tokens, repository security scanning, dedicated Inference Endpoints with multiple security configurations, runtime logs, analytics, and autoscaling.*

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