Deploy HuggingFace Models on Databricks (Custom PyFunc End-to-End Tutorial) | Project.1 скачать в хорошем качестве

Deploy HuggingFace Models on Databricks (Custom PyFunc End-to-End Tutorial) | Project.1

In this video, I show how to deploy a HuggingFace transformer model to a production endpoint using MLflow and Databricks. This tutorial walks through the complete real-world workflow used by machine learning engineers to serve transformer models reliably. We will: • Install PyTorch and transformers • Download a HuggingFace embedding model • Wrap the model inside a custom MLflow pyfunc model • Implement load_context and predict methods • Log and register the model in MLflow Model Registry • Deploy the model as a Databricks serving endpoint This workflow is commonly used in production systems such as: • Semantic search systems • RAG pipelines • Recommendation systems • AI applications Model used: sentence-transformers/paraphrase-MiniLM-L3-v2 If you're interested in ML Engineering, MLOps, or production deployment of transformer models, this tutorial will give you practical, real-world knowledge. About me: I’m Rahul Jha (datageekrj), an ML Engineer with experience deploying real-world machine learning systems across cloud platforms. I post tutorials on: • ML Engineering • MLflow • Vector databases • LLM deployment • Production ML systems Subscribe for more real-world ML tutorials.