MLOps packaging: HuggingFace and Docker Hub

Learn how to package a HuggingFace GPT2 model using automation with #MLOps and pushing the result to Docker Hub. With just a little bit of #Python and #FastAPI you can have a powerful text generation API that is self-documented! ▬▬🥇 Learn 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 Docker Hub ▬▬ C H A P T E R S ⏰ ▬▬ 00:00 Introduction 01:20 FastAPI container files 04:36 Build docker container 05:40 Run container locally 06:20 Interact with API 07:50 Create Action workflow 10:00 Authenticate to Docker Hub 10:45 Generate Tokens 11:20 Add secrets 13:07 Update workflow 13:50 Verify everything works ▬▬ 🔗 R E S O U R C E S ▬▬ Example repository → https://github.com/alfredodeza/huggingface-docker Practical MLOps book → https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017/ Packaging ML models → https://learning.oreilly.com/videos/packaging-machine-learning/50116VIDEOPAIML/