MLOps packaging: HuggingFace and GitHub Container Registry

Use automation to package models to GitHub

Learn how to package a HuggingFace GPT2 model using automation with MLOps and pushing the result to GitHub Container Registry. With just a little bit of Python and FastAPI you can have a powerful text generation API that is self-documented!

Automation is a foundational piece of MLOps, and using GitHub Actions to package a model automatically and on-demand with GitHub Actions you can create robust deployments and testing scenarios for machine learning operations.

In this video lesson, I'll go over the details with an example repository you can use for reference including the following learning objectives:

• Create a FastAPI application with HuggingFace
• Interact with the model with HTTP from a container using FastAPI
• Containerize the application using GitHub Actions
• Create repository secrets to login and push to GitHub Container Registry (ghcr.io)

• Example repository
• Practical MLOps book
• MLOps Maturity Model
• Packaging ML models