--- title: "Creating document embeddings with Hugging Face's Transformers & Amazon SageMaker" url: https://daily.dev/posts/creating-document-embeddings-with-hugging-face-s-transformers-amazon-sagemaker-6pqxygw9k source_url: https://www.philschmid.de/custom-inference-huggingface-sagemaker type: article source: "Philipp Schmid" published: 2026-05-31T07:41:00.703Z updated: 2026-05-31T08:31:00.335Z tags: ["python", "aws"] reading_time: 4 upvotes: 0 comments: 0 language: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Creating document embeddings with Hugging Face's Transformers & Amazon SageMaker **[Philipp Schmid](https://daily.dev/sources/philschmid)** · 4 min read · 0 upvotes · 0 comments ## Summary A step-by-step guide to deploying a custom real-time inference endpoint on Amazon SageMaker using Hugging Face's Inference DLCs and Sentence Transformers for generating document embeddings. Covers writing a custom inference.py script to override default model loading and prediction methods, packaging model artifacts into a model.tar.gz archive, uploading to S3, and deploying a HuggingFaceModel endpoint. Uses the sentence-transformers/all-MiniLM-L6-v2 model with mean pooling to produce 384-dimensional dense vector embeddings suitable for semantic search and clustering. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: --- Tags: [#python](https://daily.dev/tags/python), [#aws](https://daily.dev/tags/aws) [View this post on daily.dev](https://daily.dev/posts/creating-document-embeddings-with-hugging-face-s-transformers-amazon-sagemaker-6pqxygw9k)