Hugging Face models in Amazon Bedrock
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Simon Pagezy
pagezyhf
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Healthcare ML
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singhsidhukuldeep's
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about 4 hours ago
Excited to share insights from Walmart's groundbreaking semantic search system that revolutionizes e-commerce product discovery!
The team at Walmart Global Technology(the team that I am a part of 😬) has developed a hybrid retrieval system that combines traditional inverted index search with neural embedding-based search to tackle the challenging problem of tail queries in e-commerce.
Key Technical Highlights:
• The system uses a two-tower BERT architecture where one tower processes queries and another processes product information, generating dense vector representations for semantic matching.
• Product information is enriched by combining titles with key attributes like category, brand, color, and gender using special prefix tokens to help the model distinguish different attribute types.
• The neural model leverages DistilBERT with 6 layers and projects the 768-dimensional embeddings down to 256 dimensions using a linear layer, achieving optimal performance while reducing storage and computation costs.
• To improve model training, they implemented innovative negative sampling techniques combining product category matching and token overlap filtering to identify challenging negative examples.
Production Implementation Details:
• The system uses a managed ANN (Approximate Nearest Neighbor) service to enable fast retrieval, achieving 99% recall@20 with just 13ms latency.
• Query embeddings are cached with preset TTL (Time-To-Live) to reduce latency and costs in production.
• The model is exported to ONNX format and served in Java, with custom optimizations like fixed input shapes and GPU acceleration using NVIDIA T4 processors.
Results:
The system showed significant improvements in both offline metrics and live experiments, with:
- +2.84% improvement in NDCG@10 for human evaluation
- +0.54% lift in Add-to-Cart rates in live A/B testing
This is a fantastic example of how modern NLP techniques can be successfully deployed at scale to solve real-world e-
reacted
to
singhsidhukuldeep's
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with 🤯
about 4 hours ago
Excited to share insights from Walmart's groundbreaking semantic search system that revolutionizes e-commerce product discovery!
The team at Walmart Global Technology(the team that I am a part of 😬) has developed a hybrid retrieval system that combines traditional inverted index search with neural embedding-based search to tackle the challenging problem of tail queries in e-commerce.
Key Technical Highlights:
• The system uses a two-tower BERT architecture where one tower processes queries and another processes product information, generating dense vector representations for semantic matching.
• Product information is enriched by combining titles with key attributes like category, brand, color, and gender using special prefix tokens to help the model distinguish different attribute types.
• The neural model leverages DistilBERT with 6 layers and projects the 768-dimensional embeddings down to 256 dimensions using a linear layer, achieving optimal performance while reducing storage and computation costs.
• To improve model training, they implemented innovative negative sampling techniques combining product category matching and token overlap filtering to identify challenging negative examples.
Production Implementation Details:
• The system uses a managed ANN (Approximate Nearest Neighbor) service to enable fast retrieval, achieving 99% recall@20 with just 13ms latency.
• Query embeddings are cached with preset TTL (Time-To-Live) to reduce latency and costs in production.
• The model is exported to ONNX format and served in Java, with custom optimizations like fixed input shapes and GPU acceleration using NVIDIA T4 processors.
Results:
The system showed significant improvements in both offline metrics and live experiments, with:
- +2.84% improvement in NDCG@10 for human evaluation
- +0.54% lift in Add-to-Cart rates in live A/B testing
This is a fantastic example of how modern NLP techniques can be successfully deployed at scale to solve real-world e-
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a model
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Qwen/QVQ-72B-Preview
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Today you are able to access some of the most famous models from the Hugging Face community in Amazon Bedrock 🤯
Amazon Bedrock expands its model catalog with Bedrock Marketplace to hundreds of specialized models.
https://us-west-2.console.aws.amazon.com/bedrock/home?region=us-west-2#/model-catalog
Amazon Bedrock expands its model catalog with Bedrock Marketplace to hundreds of specialized models.
https://us-west-2.console.aws.amazon.com/bedrock/home?region=us-west-2#/model-catalog
Post
962
It’s 2nd of December , here’s your Cyber Monday present 🎁 !
We’re cutting our price down on Hugging Face Inference Endpoints and Spaces!
Our folks at Google Cloud are treating us with a 40% price cut on GCP Nvidia A100 GPUs for the next 3️⃣ months. We have other reductions on all instances ranging from 20 to 50%.
Sounds like the time to give Inference Endpoints a try? Get started today and find in our documentation the full pricing details.
https://ui.endpoints.huggingface.co/
https://huggingface.co/pricing
We’re cutting our price down on Hugging Face Inference Endpoints and Spaces!
Our folks at Google Cloud are treating us with a 40% price cut on GCP Nvidia A100 GPUs for the next 3️⃣ months. We have other reductions on all instances ranging from 20 to 50%.
Sounds like the time to give Inference Endpoints a try? Get started today and find in our documentation the full pricing details.
https://ui.endpoints.huggingface.co/
https://huggingface.co/pricing
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