[5/5] Huggingface to Sagemaker with ZenML Pipelines - Deploying to AWS Sagemaker Endpoints

While almost every Huggingface model can be easily deployed to AWS Sagemaker endpoints with a few lines of code, it is often desirous to automate this flow, and have this process track the entire lineage of the model as it goes from training to production.
Deployment is the ultimate step we use a ZenML pipeline to automate the deployment of the slated production model to a Sagemaker endpoint. This pipelines handles the complexities of AWS interactions and ensures that the model, along with its full history and context, is transitioned into a live environment ready for use.
💻 GitHub Repository -
github.com/zen...
📞 Questions? Ask us on Slack -
zenml.io/slack
🎥 Full Playlist -
• [1/5] Huggingface to A...
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🔥 About ZenML -
🤹 ZenML is an extensible, open-source MLOps framework for creating portable, production-ready machine learning pipelines. By decoupling infrastructure from code, ZenML enables developers across your organization to collaborate more effectively as they develop to production.
🚀 Website - zenml.io
📕 Documentation - docs.zenml.io
☁️ Cloud - cloud.zenml.io
⭐ GitHub Repository - github.com/zen...

[@pauliusztin]

Great series 🎉

[@HappyDataScience]

hello i try zenml in my ml project but i'm using docker and every time i run docker container it run's new pipeline and i'm not able to use cache any help !!!

[@HappyDataScience]

basically need help with zenml + docker, is their a way to use a cache from previous image when running the docker compose run ?

[@ZenML]

Join our slack for support: zenml.io/slack