Deploying Models with ZenML and Hugging Face Hub

This video is a recording from the ZenML Meet The Community session on 2nd November 2022. The community session happens every Wednesday at 8:30 AM PT / 5:30 PM CET / 9:00 PM IST.
The goal is to engage the community and chat about ZenML's latest features and showcase interesting demos or use cases. Sometimes we just take questions and have fun. Join us if you are curious about ZenML, or just want to talk shop about MLOps.
The session is free and open to everyone.
Register here to join 👇
zenml.io/meet
===
Deploying ML models for production is a pain. There are many things to consider like model accuracy, scalability, latency, etc.
Hugging Face Hub is a platform where you can host your models without having to worry about all that. Once your model is on the Hub, users can access your model via an inference API with HTTP requests. All that can be done in minutes!
But what if you can integrate Hugging Face Hub into your ML pipeline and connect your favorite MLOps tools together?
In our community meetup this week we will show how you can use ZenML to experiment with models and automate the deployment step to Hugging Face Hub when certain criteria are met.
Join us this week as have Safoine El khabich, ML Engineer at ZenML to show how you can go from local deployment to full-fledged production-ready deployment with Hugging Face Hub - in minutes.
⚡ You'll learn -
‣ The basics of model deployment - What, when, how?
‣ Deploying models locally with MLflow.
‣ Pushing your models to Hugging Face Hub.
⏱ Timestamps -
00:40 What is Model Deployment?
01:23 When to use it in your pipeline?
02:54 How to use it in your pipeline?
04:11 Local Deployment with MLflow
05:16 Hugging Face Deployment Step
06:04 Demo
📞 Questions? Ask us on Slack -
zenml.io/slack...
===
🔥 About ZenML -
ZenML is an extensible, open-source MLOps framework for creating portable, production-ready MLOps pipelines. It's built for Data Scientists, ML Engineers, and MLOps Developers to collaborate as they develop to production.
🚀 Website - zenml.io
📕 Documentation - docs.zenml.io
⭐ GitHub Repository - github.com/zen...
🧵 End-To-End Examples - github.com/zen...