# Amazon SageMaker

Published articles for Amazon SageMaker.

This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles.

## Build an AI-powered product tagging system with Amazon SageMaker serverless model customization

DevFeed: [Build an AI-powered product tagging system with Amazon SageMaker serverless model customization](<https://devfeed.tech/articles/build-an-ai-powered-product-tagging-system-with-amazon-sagemaker-serverless-model-customization-26940.md>)

Original publisher: [Read original article](<https://aws.amazon.com/blogs/machine-learning/build-an-ai-powered-product-tagging-system-with-amazon-sagemaker-serverless-model-customization/>)

Author: Linpo Guo

Published: 2026-09-15T16:11:36Z

Content type: tutorial

Language: en

Sources: [Artificial Intelligence](<https://devfeed.tech/sources/artificial-intelligence.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Amazon SageMaker AI](<https://devfeed.tech/topics/amazon-sagemaker-ai.md>), [Fine-tuning](<https://devfeed.tech/topics/fine-tuning.md>), [rlvr](<https://devfeed.tech/topics/rlvr.md>), [grpo](<https://devfeed.tech/topics/grpo.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [model-deployment](<https://devfeed.tech/topics/model-deployment.md>), [Reinforcement learning](<https://devfeed.tech/topics/reinforcement-learning.md>), [SDK](<https://devfeed.tech/topics/sdk.md>), [Serverless](<https://devfeed.tech/topics/serverless.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [amazon-sagemaker](<https://devfeed.tech/tags/amazon-sagemaker.md>), [amazon-sagemaker-ai](<https://devfeed.tech/tags/amazon-sagemaker-ai.md>), [aws](<https://devfeed.tech/tags/aws.md>), [compute](<https://devfeed.tech/tags/compute.md>), [cost](<https://devfeed.tech/tags/cost.md>), [customization](<https://devfeed.tech/tags/customization.md>), [expert-400](<https://devfeed.tech/tags/expert-400.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [inference](<https://devfeed.tech/tags/inference.md>), [jobs](<https://devfeed.tech/tags/jobs.md>), [reinforcement-learning](<https://devfeed.tech/tags/reinforcement-learning.md>), [rlvr](<https://devfeed.tech/tags/rlvr.md>), [sdk](<https://devfeed.tech/tags/sdk.md>), [technical-how-to](<https://devfeed.tech/tags/technical-how-to.md>)

### AI overview

This walkthrough shows how to build a product tagging system by customizing Qwen3-8B with supervised fine-tuning and reinforcement learning with verifiable rewards on Amazon SageMaker serverless model customization. It then deploys the optimized model for asynchronous inference to enrich retail catalogs.

### Source excerpt

Manually tagging thousands of catalog products is slow and inconsistent. This walkthrough shows how to customize Qwen3-8B with supervised fine-tuning (SFT) and reinforcement learning with verifiable rewards (RLVR) on Amazon SageMaker serverless model customization, then deploy it for asynchronous inference to build a cost-efficient product tagging system.

## Reduce LLM latency with prefix-aware routing on Amazon SageMaker Inference

DevFeed: [Reduce LLM latency with prefix-aware routing on Amazon SageMaker Inference](<https://devfeed.tech/articles/reduce-llm-latency-with-prefix-aware-routing-on-amazon-sagemaker-inference-4740.md>)

Original publisher: [Read original article](<https://aws.amazon.com/blogs/machine-learning/reduce-llm-latency-with-prefix-aware-routing-on-amazon-sagemaker-inference/>)

Author: Kareem Syed-Mohammed

Published: 2026-09-10T21:58:09Z

Content type: release

Language: en

Sources: [Artificial Intelligence](<https://devfeed.tech/sources/artificial-intelligence.md>)

Topics: [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Low-Latency Inference](<https://devfeed.tech/topics/low-latency-inference.md>), [Caching](<https://devfeed.tech/topics/caching.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [Inference Performance](<https://devfeed.tech/topics/inference-performance.md>)

Tags: [advanced-300](<https://devfeed.tech/tags/advanced-300.md>), [amazon-sagemaker](<https://devfeed.tech/tags/amazon-sagemaker.md>), [amazon-sagemaker-ai](<https://devfeed.tech/tags/amazon-sagemaker-ai.md>), [announcements](<https://devfeed.tech/tags/announcements.md>), [benchmarks](<https://devfeed.tech/tags/benchmarks.md>), [caching](<https://devfeed.tech/tags/caching.md>), [inference](<https://devfeed.tech/tags/inference.md>), [latency](<https://devfeed.tech/tags/latency.md>), [llama](<https://devfeed.tech/tags/llama.md>), [llm](<https://devfeed.tech/tags/llm.md>), [routing](<https://devfeed.tech/tags/routing.md>), [tensorrt-llm](<https://devfeed.tech/tags/tensorrt-llm.md>), [vllm](<https://devfeed.tech/tags/vllm.md>)

### AI overview

Amazon SageMaker Inference introduces prefix-aware routing for LLM requests. By consistently sending requests with matching prompt prefixes to the same instance, it improves reuse of cached KV computations and can reduce time to first token.

### Source excerpt

Amazon SageMaker Inference now offers prefix-aware routing, a routing strategy that sends requests sharing the same prompt prefix to the same instance so the KV cache stays warm. In benchmarks on Llama 3.1 70B, it reduced P50 time-to-first-token by up to 77% and raised KV cache hit rates from about 25% to over 80%.

## Reduce inference cold starts on Amazon SageMaker HyperPod with model caching

DevFeed: [Reduce inference cold starts on Amazon SageMaker HyperPod with model caching](<https://devfeed.tech/articles/reduce-inference-cold-starts-on-amazon-sagemaker-hyperpod-with-model-caching-4739.md>)

Original publisher: [Read original article](<https://aws.amazon.com/blogs/machine-learning/reduce-inference-cold-starts-on-amazon-sagemaker-hyperpod-with-model-caching/>)

Author: Kareem Syed-Mohammed

Published: 2026-09-10T21:37:49Z

Content type: tutorial

Language: en

Sources: [Artificial Intelligence](<https://devfeed.tech/sources/artificial-intelligence.md>)

Topics: [Caching](<https://devfeed.tech/topics/caching.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [model-deployment](<https://devfeed.tech/topics/model-deployment.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [autoscaling](<https://devfeed.tech/topics/autoscaling.md>), [Kubernetes](<https://devfeed.tech/topics/kubernetes.md>), [Amazon S3](<https://devfeed.tech/topics/amazon-s3.md>)

Tags: [amazon](<https://devfeed.tech/tags/amazon.md>), [amazon-s3](<https://devfeed.tech/tags/amazon-s3.md>), [amazon-sagemaker](<https://devfeed.tech/tags/amazon-sagemaker.md>), [amazon-sagemaker-hyperpod](<https://devfeed.tech/tags/amazon-sagemaker-hyperpod.md>), [announcements](<https://devfeed.tech/tags/announcements.md>), [autoscaling](<https://devfeed.tech/tags/autoscaling.md>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [caching](<https://devfeed.tech/tags/caching.md>), [expert-400](<https://devfeed.tech/tags/expert-400.md>), [inference](<https://devfeed.tech/tags/inference.md>), [kubernetes](<https://devfeed.tech/tags/kubernetes.md>), [llm](<https://devfeed.tech/tags/llm.md>)

### AI overview

Amazon SageMaker HyperPod model caching preloads model weights and container images onto cluster nodes, reducing inference-pod cold starts by serving assets from local NVMe storage.

### Source excerpt

Amazon SageMaker HyperPod now supports model caching for inference, which pre-loads model weights and container images onto cluster nodes so pods read from local NVMe storage instead of downloading over the network. Learn how model caching cuts cold starts from tens of minutes to seconds, how it works, and how to enable it.

## Deploying Qwen3.8-2.4T-A95B on Amazon SageMaker HyperPod with vLLM

DevFeed: [Deploying Qwen3.8-2.4T-A95B on Amazon SageMaker HyperPod with vLLM](<https://devfeed.tech/articles/deploying-qwen3-8-2-4t-a95b-on-amazon-sagemaker-hyperpod-with-vllm-4731.md>)

Original publisher: [Read original article](<https://aws.amazon.com/blogs/machine-learning/deploying-qwen3-8-2-4t-a95b-on-amazon-sagemaker-hyperpod-with-vllm/>)

Author: Dmitry Soldatkin

Published: 2026-09-09T22:26:29Z

Content type: tutorial

Language: en

Sources: [Artificial Intelligence](<https://devfeed.tech/sources/artificial-intelligence.md>)

Topics: [Deployment](<https://devfeed.tech/topics/deployment.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [Language models](<https://devfeed.tech/topics/language-models.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [long-context](<https://devfeed.tech/topics/long-context.md>), [Mixture of Experts (MoE)](<https://devfeed.tech/topics/mixture-of-experts-moe.md>)

Tags: [advanced-300](<https://devfeed.tech/tags/advanced-300.md>), [agentic](<https://devfeed.tech/tags/agentic.md>), [amazon-sagemaker](<https://devfeed.tech/tags/amazon-sagemaker.md>), [amazon-sagemaker-hyperpod](<https://devfeed.tech/tags/amazon-sagemaker-hyperpod.md>), [deployment](<https://devfeed.tech/tags/deployment.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [how-to](<https://devfeed.tech/tags/how-to.md>), [inference](<https://devfeed.tech/tags/inference.md>), [long-context](<https://devfeed.tech/tags/long-context.md>), [moe](<https://devfeed.tech/tags/moe.md>), [nvfp4](<https://devfeed.tech/tags/nvfp4.md>), [quantization](<https://devfeed.tech/tags/quantization.md>), [qwen](<https://devfeed.tech/tags/qwen.md>), [reasoning](<https://devfeed.tech/tags/reasoning.md>), [technical-how-to](<https://devfeed.tech/tags/technical-how-to.md>), [tool](<https://devfeed.tech/tags/tool.md>), [vllm](<https://devfeed.tech/tags/vllm.md>)

### AI overview

A deployment walkthrough for serving the open-weight Qwen3.8-2.4T-A95B language model on Amazon SageMaker HyperPod with vLLM and NVIDIA B300 GPUs. It covers provisioning, NVFP4 quantization, an OpenAI-compatible endpoint, reasoning, tool calling, and MTP speculative decoding.

### Source excerpt

Learn how to deploy Qwen3.8-2.4T-A95B, a 2.4-trillion-parameter open-weight model, on Amazon SageMaker HyperPod with vLLM. This walkthrough covers cluster provisioning, NVFP4 quantization, and an OpenAI-compatible endpoint with built-in reasoning, tool calling, and native MTP speculative decoding.

## Pathway's brain-inspired architecture development on Amazon SageMaker HyperPod

DevFeed: [Pathway's brain-inspired architecture development on Amazon SageMaker HyperPod](<https://devfeed.tech/articles/pathway-s-brain-inspired-architecture-development-on-amazon-sagemaker-hyperpod-4738.md>)

Original publisher: [Read original article](<https://aws.amazon.com/blogs/machine-learning/pathway-s-brain-inspired-architecture-development-on-amazon-sagemaker-hyperpod/>)

Author: Paulo Aragão

Published: 2026-09-08T19:12:51Z

Content type: article

Language: en

Sources: [Artificial Intelligence](<https://devfeed.tech/sources/artificial-intelligence.md>)

Topics: [Language models](<https://devfeed.tech/topics/language-models.md>), [Training AI Models](<https://devfeed.tech/topics/training-ai-models.md>), [compression and generalization](<https://devfeed.tech/topics/compression-and-generalization.md>), [LLM evaluation / benchmarking](<https://devfeed.tech/topics/llm-evaluation-benchmarking.md>)

Tags: [amazon](<https://devfeed.tech/tags/amazon.md>), [amazon-sagemaker](<https://devfeed.tech/tags/amazon-sagemaker.md>), [amazon-sagemaker-hyperpod](<https://devfeed.tech/tags/amazon-sagemaker-hyperpod.md>), [announcements](<https://devfeed.tech/tags/announcements.md>), [architecture](<https://devfeed.tech/tags/architecture.md>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [chain-of-thought](<https://devfeed.tech/tags/chain-of-thought.md>), [distributed-training](<https://devfeed.tech/tags/distributed-training.md>), [intermediate-200](<https://devfeed.tech/tags/intermediate-200.md>), [pytorch](<https://devfeed.tech/tags/pytorch.md>), [transformer-architecture](<https://devfeed.tech/tags/transformer-architecture.md>)

### AI overview

Pathway describes BDH, a brain-inspired architecture that performs reasoning in latent space rather than producing chain-of-thought token traces. The article covers its recurrent internal memory, its contrast with transformer limitations, and scaling training with Amazon SageMaker HyperPod.

### Source excerpt

Pathway's Baby Dragon Hatchling (BDH) is a brain-inspired, post-transformer architecture that reasons in latent space instead of emitting chain-of-thought tokens. See how Pathway develops and scales BDH on Amazon SageMaker HyperPod, and how BDH-CQ set a new cost-efficiency mark on the ARC-AGI-1 benchmark.

## Amazon SageMaker Feature Store introduces UpdateRecord for feature-level writes

DevFeed: [Amazon SageMaker Feature Store introduces UpdateRecord for feature-level writes](<https://devfeed.tech/articles/amazon-sagemaker-feature-store-introduces-updaterecord-for-feature-level-writes-4726.md>)

Original publisher: [Read original article](<https://aws.amazon.com/blogs/machine-learning/amazon-sagemaker-feature-store-introduces-updaterecord-for-feature-level-writes/>)

Author: Mona Mona

Published: 2026-09-08T18:29:15Z

Content type: release

Language: en

Sources: [Artificial Intelligence](<https://devfeed.tech/sources/artificial-intelligence.md>)

Topics: [AI, ML & Data Engineering](<https://devfeed.tech/topics/ai-ml-data-engineering.md>), [Training AI Models](<https://devfeed.tech/topics/training-ai-models.md>), [AWS IAM](<https://devfeed.tech/topics/aws-iam.md>)

Tags: [amazon-dynamodb](<https://devfeed.tech/tags/amazon-dynamodb.md>), [amazon-elasticache](<https://devfeed.tech/tags/amazon-elasticache.md>), [amazon-machine-learning](<https://devfeed.tech/tags/amazon-machine-learning.md>), [amazon-sagemaker](<https://devfeed.tech/tags/amazon-sagemaker.md>), [amazon-sagemaker-ai](<https://devfeed.tech/tags/amazon-sagemaker-ai.md>), [announcements](<https://devfeed.tech/tags/announcements.md>), [api](<https://devfeed.tech/tags/api.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [aws-identity-and-access-management-iam](<https://devfeed.tech/tags/aws-identity-and-access-management-iam.md>), [intermediate-200](<https://devfeed.tech/tags/intermediate-200.md>), [technical-how-to](<https://devfeed.tech/tags/technical-how-to.md>)

### AI overview

Amazon SageMaker Feature Store adds the UpdateRecord API for atomic feature-level updates without reading or rewriting an entire record.

### Source excerpt

Amazon SageMaker Feature Store now supports feature-level writes. With the new UpdateRecord API, you can update one or more feature values in a single call without reading or rewriting the entire record. It is available for both the Standard (Amazon DynamoDB) and In-Memory (Amazon ElastiCache) online store tiers.

## AWS Weekly Roundup: Welcome DuckLabs to the team, Agentic Resource Discovery (ARD), and more (August 31, 2026)

DevFeed: [AWS Weekly Roundup: Welcome DuckLabs to the team, Agentic Resource Discovery (ARD), and more (August 31, 2026)](<https://devfeed.tech/articles/aws-weekly-roundup-welcome-ducklabs-to-the-team-agentic-resource-discovery-ard-and-more-august-31-2026-4618.md>)

Original publisher: [Read original article](<https://aws.amazon.com/blogs/aws/aws-weekly-roundup-welcome-ducklabs-to-the-team-agentic-resource-discovery-ard-and-more-august-31-2026/>)

Author: Daniel Abib

Published: 2026-08-31T14:45:25Z

Content type: news

Language: en

Sources: [AWS News Blog](<https://devfeed.tech/sources/aws-news-blog.md>)

Topics: [data](<https://devfeed.tech/topics/data.md>), [Amazon S3](<https://devfeed.tech/topics/amazon-s3.md>), [Amazon Redshift](<https://devfeed.tech/topics/amazon-redshift.md>), [Node.js](<https://devfeed.tech/topics/node-js.md>)

Tags: [acquisition](<https://devfeed.tech/tags/acquisition.md>), [amazon-ec2](<https://devfeed.tech/tags/amazon-ec2.md>), [amazon-elastic-container-service](<https://devfeed.tech/tags/amazon-elastic-container-service.md>), [amazon-gamelift](<https://devfeed.tech/tags/amazon-gamelift.md>), [amazon-redshift](<https://devfeed.tech/tags/amazon-redshift.md>), [amazon-s3](<https://devfeed.tech/tags/amazon-s3.md>), [amazon-sagemaker](<https://devfeed.tech/tags/amazon-sagemaker.md>), [amazon-sagemaker-ai](<https://devfeed.tech/tags/amazon-sagemaker-ai.md>), [analytics](<https://devfeed.tech/tags/analytics.md>), [announcements](<https://devfeed.tech/tags/announcements.md>), [aws](<https://devfeed.tech/tags/aws.md>), [aws-fargate](<https://devfeed.tech/tags/aws-fargate.md>), [aws-glue](<https://devfeed.tech/tags/aws-glue.md>), [aws-iot-core](<https://devfeed.tech/tags/aws-iot-core.md>), [aws-lambda](<https://devfeed.tech/tags/aws-lambda.md>), [database](<https://devfeed.tech/tags/database.md>), [duckdb](<https://devfeed.tech/tags/duckdb.md>), [json](<https://devfeed.tech/tags/json.md>), [news](<https://devfeed.tech/tags/news.md>), [node-js](<https://devfeed.tech/tags/node-js.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [parquet](<https://devfeed.tech/tags/parquet.md>), [python](<https://devfeed.tech/tags/python.md>), [sql](<https://devfeed.tech/tags/sql.md>), [week-in-review](<https://devfeed.tech/tags/week-in-review.md>)

### AI overview

AWS weekly roundup covering the planned acquisition of DuckLabs, the company behind DuckDB, alongside Amazon ECS recovery updates and AWS Lambda preview runtimes for Node.js 26 and Python 3.15.

### Source excerpt

The news that interested me the most last week was the DuckLabs acquisition. AWS has signed a definitive agreement to acquire DuckLabs, the Amsterdam-based company behind DuckDB, the popular open source analytical database that runs in-process and executes SQL directly against files like Parquet, CSV, and JSON. DuckDB stays open source under its independent foundation [...]

## AWS Weekly Roundup: AWS Builder Center at 1 year, Network Scanning in Security Hub, Loom for AWS, and more (July 13, 2026)

DevFeed: [AWS Weekly Roundup: AWS Builder Center at 1 year, Network Scanning in Security Hub, Loom for AWS, and more (July 13, 2026)](<https://devfeed.tech/articles/aws-weekly-roundup-aws-builder-center-at-1-year-network-scanning-in-security-hub-loom-for-aws-and-more-july-13-2026-4609.md>)

Original publisher: [Read original article](<https://aws.amazon.com/blogs/aws/aws-weekly-roundup-aws-builder-center-at-one-year-network-scanning-in-security-hub-loom-for-aws-and-more-july-13-2026/>)

Author: Esra Kayabali

Published: 2026-07-13T16:18:20Z

Content type: news

Language: en

Sources: [AWS News Blog](<https://devfeed.tech/sources/aws-news-blog.md>)

Topics: [Amazon Web Services](<https://devfeed.tech/topics/aws.md>), [Security](<https://devfeed.tech/topics/security.md>)

Tags: [agents](<https://devfeed.tech/tags/agents.md>), [amazon-aurora](<https://devfeed.tech/tags/amazon-aurora.md>), [amazon-elastic-container-service](<https://devfeed.tech/tags/amazon-elastic-container-service.md>), [amazon-elastic-kubernetes-service](<https://devfeed.tech/tags/amazon-elastic-kubernetes-service.md>), [amazon-sagemaker](<https://devfeed.tech/tags/amazon-sagemaker.md>), [announcements](<https://devfeed.tech/tags/announcements.md>), [aws](<https://devfeed.tech/tags/aws.md>), [aws-security-hub](<https://devfeed.tech/tags/aws-security-hub.md>), [community](<https://devfeed.tech/tags/community.md>), [ecosystem](<https://devfeed.tech/tags/ecosystem.md>), [github](<https://devfeed.tech/tags/github.md>), [hub](<https://devfeed.tech/tags/hub.md>), [launch](<https://devfeed.tech/tags/launch.md>), [linux](<https://devfeed.tech/tags/linux.md>), [mcp](<https://devfeed.tech/tags/mcp.md>), [news](<https://devfeed.tech/tags/news.md>), [platform](<https://devfeed.tech/tags/platform.md>), [sandbox](<https://devfeed.tech/tags/sandbox.md>), [security](<https://devfeed.tech/tags/security.md>), [spaces](<https://devfeed.tech/tags/spaces.md>), [week-in-review](<https://devfeed.tech/tags/week-in-review.md>), [workshops](<https://devfeed.tech/tags/workshops.md>)

### AI overview

AWS Weekly Roundup highlights the first anniversary of AWS Builder Center, including its expansion into sandbox environments, workshops, Spaces, community features, and the Builders' Library. It also covers the launch of Network Scanning in AWS Security Hub, which probes resources from the public internet to identify actual reachability, alongside other weekly AWS announcements.

### Source excerpt

AWS Builder Center turned one year old last week. Launched on July 9, 2025, the platform has grown from a community hub with Wishlist voting, community profiles, and a toolbox into a full ecosystem with sandbox environments, workshops, Spaces, and a Builders' Library. To mark the anniversary, Rick Suttles published a full feature timeline covering [...]

## How to deploy and fine-tune DeepSeek models on AWS

DevFeed: [How to deploy and fine-tune DeepSeek models on AWS](<https://devfeed.tech/articles/how-to-deploy-and-fine-tune-deepseek-models-on-aws-7161.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/deepseek-r1-aws>)

Author: Simon Pagezy; Jeff Boudier; David Corvoysier

Published: 2025-01-30T00:00:00Z

Content type: tutorial

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [deepseek](<https://devfeed.tech/topics/deepseek.md>), [inference-endpoints](<https://devfeed.tech/topics/inference-endpoints.md>), [Amazon Web Services](<https://devfeed.tech/topics/aws.md>), [hugging face](<https://devfeed.tech/topics/hugging-face.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>), [Amazon Bedrock](<https://devfeed.tech/topics/amazon-bedrock.md>), [Amazon SageMaker AI](<https://devfeed.tech/topics/amazon-sagemaker-ai.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [autoscaling](<https://devfeed.tech/topics/autoscaling.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>)

Tags: [amazon-bedrock](<https://devfeed.tech/tags/amazon-bedrock.md>), [amazon-sagemaker](<https://devfeed.tech/tags/amazon-sagemaker.md>), [amazon-sagemaker-ai](<https://devfeed.tech/tags/amazon-sagemaker-ai.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [autoscaling](<https://devfeed.tech/tags/autoscaling.md>), [aws](<https://devfeed.tech/tags/aws.md>), [deepseek](<https://devfeed.tech/tags/deepseek.md>), [deployment](<https://devfeed.tech/tags/deployment.md>), [generative-ai](<https://devfeed.tech/tags/generative-ai.md>), [how-to](<https://devfeed.tech/tags/how-to.md>), [inference](<https://devfeed.tech/tags/inference.md>), [inference-endpoints](<https://devfeed.tech/tags/inference-endpoints.md>), [llms](<https://devfeed.tech/tags/llms.md>), [models](<https://devfeed.tech/tags/models.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [partnerships](<https://devfeed.tech/tags/partnerships.md>)

### AI overview

This tutorial explains how to deploy and fine-tune DeepSeek-R1 and its distilled models on AWS using Hugging Face Inference Endpoints. It covers production deployment with dedicated compute, autoscaling, scale-to-zero, security, optimized hardware, and deployment options through Amazon Bedrock and Amazon SageMaker AI.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## Hugging Face models in Amazon Bedrock

DevFeed: [Hugging Face models in Amazon Bedrock](<https://devfeed.tech/articles/hugging-face-models-in-amazon-bedrock-7125.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/bedrock-marketplace>)

Author: Simon Pagezy; Philipp Schmid; Jeff Boudier; Violette

Published: 2024-12-09T00:00:00Z

Content type: tutorial

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [Amazon Bedrock](<https://devfeed.tech/topics/amazon-bedrock.md>), [hugging face](<https://devfeed.tech/topics/hugging-face.md>), [Amazon SageMaker JumpStart](<https://devfeed.tech/topics/amazon-sagemaker-jumpstart.md>), [gemma](<https://devfeed.tech/topics/gemma.md>), [Generative AI](<https://devfeed.tech/topics/generative-ai.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>), [Google](<https://devfeed.tech/topics/google.md>)

Tags: [amazon-bedrock](<https://devfeed.tech/tags/amazon-bedrock.md>), [amazon-sagemaker](<https://devfeed.tech/tags/amazon-sagemaker.md>), [amazon-sagemaker-jumpstart](<https://devfeed.tech/tags/amazon-sagemaker-jumpstart.md>), [api](<https://devfeed.tech/tags/api.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [aws](<https://devfeed.tech/tags/aws.md>), [bedrock](<https://devfeed.tech/tags/bedrock.md>), [cloud](<https://devfeed.tech/tags/cloud.md>), [deployment](<https://devfeed.tech/tags/deployment.md>), [gemma](<https://devfeed.tech/tags/gemma.md>), [generative-ai](<https://devfeed.tech/tags/generative-ai.md>), [hugging-face](<https://devfeed.tech/tags/hugging-face.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [partnerships](<https://devfeed.tech/tags/partnerships.md>)

### AI overview

This tutorial explains how to deploy open Hugging Face models, including Google Gemma 2 27B Instruct, through the Amazon Bedrock Marketplace. It covers model selection, deployment, endpoint configuration, Bedrock API usage, and cleanup, with model endpoints managed by Amazon SageMaker JumpStart.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## Introducing the Hugging Face Embedding Container for Amazon SageMaker

DevFeed: [Introducing the Hugging Face Embedding Container for Amazon SageMaker](<https://devfeed.tech/articles/introducing-the-hugging-face-embedding-container-for-amazon-sagemaker-7464.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/sagemaker-huggingface-embedding>)

Author: Philipp Schmid; Jeff Boudier

Published: 2024-06-07T00:00:00Z

Content type: tutorial

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [model-deployment](<https://devfeed.tech/topics/model-deployment.md>), [Amazon SageMaker](<https://devfeed.tech/topics/amazon-sagemaker.md>), [Amazon Bedrock](<https://devfeed.tech/topics/amazon-bedrock.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [Embeddings](<https://devfeed.tech/topics/embeddings.md>), [Transformers](<https://devfeed.tech/topics/transformers.md>), [sentence-transformers](<https://devfeed.tech/topics/sentence-transformers.md>), [AWS IAM](<https://devfeed.tech/topics/aws-iam.md>)

Tags: [amazon-sagemaker](<https://devfeed.tech/tags/amazon-sagemaker.md>), [aws](<https://devfeed.tech/tags/aws.md>), [batching](<https://devfeed.tech/tags/batching.md>), [cloud](<https://devfeed.tech/tags/cloud.md>), [container](<https://devfeed.tech/tags/container.md>), [embedding](<https://devfeed.tech/tags/embedding.md>), [embeddings](<https://devfeed.tech/tags/embeddings.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [guide](<https://devfeed.tech/tags/guide.md>), [hugging-face](<https://devfeed.tech/tags/hugging-face.md>), [inference](<https://devfeed.tech/tags/inference.md>), [partnership](<https://devfeed.tech/tags/partnership.md>), [prometheus](<https://devfeed.tech/tags/prometheus.md>), [sdk](<https://devfeed.tech/tags/sdk.md>), [sentence-transformers](<https://devfeed.tech/tags/sentence-transformers.md>), [telemetry](<https://devfeed.tech/tags/telemetry.md>), [tracing](<https://devfeed.tech/tags/tracing.md>)

### AI overview

This tutorial explains how to deploy open embedding models to Amazon SageMaker using the Hugging Face Embedding Container. It uses Text Embeddings Inference for efficient, production-ready serving and covers container selection, CPU and GPU variants, batching, and observability features.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## Welcome Llama 3 - Meta's new open LLM

DevFeed: [Welcome Llama 3 - Meta's new open LLM](<https://devfeed.tech/articles/welcome-llama-3-meta-s-new-open-llm-7334.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/llama3>)

Author: Philipp Schmid; Omar Sanseviero; Pedro Cuenca; Younes B; Leandro von Werra

Published: 2024-04-18T00:00:00Z

Content type: release

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [llama](<https://devfeed.tech/topics/llama.md>), [Meta](<https://devfeed.tech/topics/meta.md>), [hugging face](<https://devfeed.tech/topics/hugging-face.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>), [Fine-tuning](<https://devfeed.tech/topics/fine-tuning.md>), [Transformers](<https://devfeed.tech/topics/transformers.md>), [trl](<https://devfeed.tech/topics/trl.md>), [Google AI](<https://devfeed.tech/topics/google-ai.md>), [Amazon SageMaker](<https://devfeed.tech/topics/amazon-sagemaker.md>), [Google Cloud Platform (GCP)](<https://devfeed.tech/topics/google-cloud.md>)

Tags: [amazon-sagemaker](<https://devfeed.tech/tags/amazon-sagemaker.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [community](<https://devfeed.tech/tags/community.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [google-cloud](<https://devfeed.tech/tags/google-cloud.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [hardware](<https://devfeed.tech/tags/hardware.md>), [hugging-face](<https://devfeed.tech/tags/hugging-face.md>), [inference](<https://devfeed.tech/tags/inference.md>), [inference-endpoints](<https://devfeed.tech/tags/inference-endpoints.md>), [llama](<https://devfeed.tech/tags/llama.md>), [llm](<https://devfeed.tech/tags/llm.md>), [meta](<https://devfeed.tech/tags/meta.md>), [nlp](<https://devfeed.tech/tags/nlp.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [research](<https://devfeed.tech/tags/research.md>), [tokens](<https://devfeed.tech/tags/tokens.md>), [transformers](<https://devfeed.tech/tags/transformers.md>), [trl](<https://devfeed.tech/tags/trl.md>)

### AI overview

Meta has released Llama 3, an open-access LLM family available through Hugging Face. The release includes 8B and 70B base and instruction-tuned models, plus Llama Guard 2 for classifying potentially unsafe LLM inputs and responses. It also adds integrations with Transformers, Hugging Chat, Inference Endpoints, Google Cloud, Amazon SageMaker, and TRL.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.