Скачать или смотреть [1/5] Huggingface to AWS Sagemaker with ZenML Pipelines - Introduction to the MLOps process

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.

This project showcases one way of using ZenML pipelines to achieve this:

Create and version a dataset in a feature_engineering_pipeline.
Train/Finetune a BERT-based Sentiment Analysis NLP model and push to Huggingface Hub in a training_pipeline.
Promote this model to Production by comparing to previous models in a promotion_pipeline.
Deploy the model at the Production Stage to a AWS Sagemaker endpoint with a deployment_pipeline.

💻 GitHub Repository -
https://github.com/zenml-io/zenml-hug...

📞 Questions? Ask us on Slack -
https://zenml.io/slack

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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 - https://zenml.io
📕 Documentation - https://docs.zenml.io
☁️ Cloud - https://cloud.zenml.io
⭐ GitHub Repository - https://github.com/zenml-io/zenml