Tool or Technology
TT0174

Hugging Face Foundations

Discover, Use & Deploy Open AI Models

Hugging Face
Duration
8 Hours
Level
Basic
Delivery
Instructor-Led
Format
Workshop

Programme Objectives

  • Build foundational proficiency in navigating the Hugging Face ecosystem and working with open AI/ML models.
  • Discover, evaluate, and select appropriate models and datasets based on task, licence, documentation, limitations, and performance.
  • Use Hugging Face Transformers to run pre-trained models for common AI tasks.
  • Access hosted models through Hugging Face Inference Providers and integrate inference into simple applications.
  • Build and publish practical AI demonstrations using Hugging Face Spaces while applying responsible model-selection and deployment practices.

Technology covered

Hugging Face HubTransformersDatasetsModel CardsDataset CardsHugging Face Hub APIInference ProvidersInference PlaygroundGradioHugging Face SpacesInference Endpoints

Who should attend

  • AI & Machine Learning Engineers
  • Data Scientists
  • Python Developers
  • Generative AI Developers
  • Software Developers
  • Data & Analytics Professionals
  • NLP Engineers
  • Computer Vision Developers
  • AI Application Developers
  • MLOps & Platform Engineers
  • Technical Leads & AI Solution Teams

Prerequisites & Participant Readiness

  • Basic Python programming knowledge
  • Basic understanding of AI and machine-learning concepts
  • Familiarity with notebooks or development environments is helpful
  • Basic understanding of APIs is beneficial but not mandatory
  • Basic Git knowledge is helpful for Hub and Spaces activities
  • No previous Hugging Face experience required
  • No prior model-training or deep-learning expertise required

TOC Modules

Concepts
  • Understanding the Hugging Face ecosystem for open AI and machine learning
  • Understanding Models, Datasets, Spaces, and repositories on the Hugging Face Hub
  • Understanding common AI tasks across text, image, audio, and multimodal models
  • Understanding public, gated, and private model resources
  • Understanding local inference versus hosted inference
Practical activities
  • Creating and exploring a Hugging Face account and workspace
  • Navigating Models, Datasets, and Spaces
  • Searching for models for a defined AI requirement
  • Comparing several model repositories for the same task

Scenarios

Business Requirement to AI-Powered Application

Business Requirement → Hugging Face Model Search → Model Card Review → Model Testing → Transformers / Inference Provider → Gradio Interface → Hugging Face Space

Participants identify an appropriate open model for a defined business requirement, evaluate its documentation and limitations, test its performance, and build a simple interactive AI application.

Model Comparison to Deployment Recommendation

AI Use Case → Candidate Models → Licence & Model Card Review → Representative Dataset → Output Evaluation → Model Comparison → Deployment Recommendation

Participants compare multiple Hugging Face models against representative business data and recommend the most suitable model based on quality, documentation, licensing, resource requirements, and deployment considerations.

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