[2/5] Huggingface to Sagemaker with ZenML Pipelines - Feature engineering with a Huggingface Dataset

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.
Automated feature engineering forms the foundation of this MLOps workflow. Data transformation steps are encoded into pipelines, ensuring reproducibility and version control for every dataset iteration. This rigor sets the stage for consistent training and evaluation.
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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...