Deep Learning Lab
Guide
Module 7 · 7 lessons · 3.5 hours in class

Pretrained Models and the Hugging Face Ecosystem

How can AI engineers leverage models that have already learned useful representations?

You will learn

  • Pretrained weights & checkpoints
  • Hugging Face Hub
  • Model cards & selection
  • Explicit inference
  • Semantic search
  • CLIP
  • Application validation
  • Deployment constraints

Hands-on: Build pretrained applications and make a requirement-driven model selection with measured evidence.

Learning route: Lessons 7.1–7.5 are classroom material. Lessons 7.6–7.7 are complete further-learning notes.

Lessons

  1. Lesson 7.1: Understanding Pretrained Models

    How can AI engineers leverage models that have already learned useful representations?

    Classroom · ~56 min self-study · 10 sections · 28 code cells
    Not started Start

  2. Lesson 7.2: The Hugging Face Ecosystem (Essentials)

    What does the Hugging Face ecosystem give us, and what exactly are we downloading when we load a model by name?

    Classroom · ~47 min self-study · 8 sections · 25 code cells
    Not started Start

  3. Lesson 7.3: Finding and Selecting the Right Model

    Given a problem, how do we find a pretrained model that fits it, and how do we tell whether its advertised performance will hold for us?

    Classroom · ~39 min self-study · 8 sections · 17 code cells
    Not started Start

  4. Lesson 7.4: Loading and Using Pretrained Models

    What happens between loading a model by name and getting a trustworthy prediction, and how do we control each step?

    Classroom · ~43 min self-study · 10 sections · 20 code cells · 1 lab
    Not started Start

  5. Lesson 7.5: Building Applications with Pretrained Models

    How do we turn a pretrained model's capability into a feature that behaves sensibly on real, messy inputs?

    Classroom · ~48 min self-study · 10 sections · 22 code cells · 1 lab
    Not started Start

  6. Lesson 7.6: Inference and Deployment Constraints

    What does it take to run a pretrained model as a reliable, affordable feature, and which levers do we have?

    Further learning · ~48 min self-study · 11 sections · 20 code cells · 1 lab
    Not started Start

  7. Lesson 7.7: Practical Model Selection and Integration

    How do we choose a pretrained model for a real task, and justify that choice with evidence?

    Further learning · ~36 min self-study · 8 sections · 20 code cells
    Not started Start

Module challenge

Earns the Module 7 badge

Module 7 challenge: Pretrained Models and the Hugging Face Ecosystem

8 scenario questions that mix ideas from all 7 lessons. One question at a time, an explanation after each, and your score at the end. Pass mark 75%, so 6 of 8.

not attempted

Best after you have practised all 7 lessons.