# Amazon SageMaker

Amazon SageMaker is an AWS platform providing managed infrastructure, tools, and workflows for analytics and for building, training, and deploying machine-learning and foundation models.

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## Enhancing industrial safety AI with synthetic data on Amazon SageMaker AI

DevFeed: [Enhancing industrial safety AI with synthetic data on Amazon SageMaker AI](<https://devfeed.tech/articles/enhancing-industrial-safety-ai-with-synthetic-data-on-amazon-sagemaker-ai-42130.md>)

Original publisher: [Read original article](<https://aws.amazon.com/blogs/machine-learning/enhancing-industrial-safety-ai-with-synthetic-data-on-amazon-sagemaker-ai/>)

Author: Dimitri Voytan

Published: 2026-09-17T15:28:08Z

Content type: tutorial

Language: en

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

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [synthetic-data](<https://devfeed.tech/topics/synthetic-data.md>), [Amazon SageMaker](<https://devfeed.tech/topics/amazon-sagemaker.md>), [data augmentation](<https://devfeed.tech/topics/data-augmentation.md>), [object-detection](<https://devfeed.tech/topics/object-detection.md>), [Person Detection](<https://devfeed.tech/topics/person-detection.md>), [Computer vision](<https://devfeed.tech/topics/computer-vision.md>), [Training AI Models](<https://devfeed.tech/topics/training-ai-models.md>), [data-processing](<https://devfeed.tech/topics/data-processing.md>), [Deployment](<https://devfeed.tech/topics/deployment.md>)

Tags: [advanced-300](<https://devfeed.tech/tags/advanced-300.md>), [ai](<https://devfeed.tech/tags/ai.md>), [amazon-sagemaker-ai](<https://devfeed.tech/tags/amazon-sagemaker-ai.md>), [computer-vision](<https://devfeed.tech/tags/computer-vision.md>), [data-augmentation](<https://devfeed.tech/tags/data-augmentation.md>), [industrial](<https://devfeed.tech/tags/industrial.md>), [object-detection](<https://devfeed.tech/tags/object-detection.md>), [person-detection](<https://devfeed.tech/tags/person-detection.md>), [pipeline](<https://devfeed.tech/tags/pipeline.md>), [safety](<https://devfeed.tech/tags/safety.md>), [synthetic-data](<https://devfeed.tech/tags/synthetic-data.md>), [technical-how-to](<https://devfeed.tech/tags/technical-how-to.md>), [training](<https://devfeed.tech/tags/training.md>), [up](<https://devfeed.tech/tags/up.md>)

### AI overview

This tutorial shows how to build a synthetic data augmentation pipeline with Amazon SageMaker AI and Amazon Rekognition for industrial safety computer vision. The pipeline generates photo-realistic, automatically labeled training images for rare and hazardous scenarios near heavy machinery, with experiments reporting up to a 160% improvement in person-detection mAP50 without manual annotation or hazardous photography sessions.

### Source excerpt

Learn how to build a synthetic data augmentation pipeline on Amazon SageMaker AI and Amazon Rekognition that generates photo-realistic, auto-labeled training images for industrial safety AI. This approach improved person detection by up to 160% without manual annotation or hazardous data collection near heavy machinery.

## Fraud Doesn't Sleep--Your Infrastructure Can't Either

DevFeed: [Fraud Doesn't Sleep--Your Infrastructure Can't Either](<https://devfeed.tech/articles/fraud-doesn-t-sleep-your-infrastructure-can-t-either-23786.md>)

Original publisher: [Read original article](<https://cockroachlabs.com/blog/fraud-doesnt-sleep-infrastructure-can't>)

Author: Harsh Shah

Published: 2026-02-04T00:00:00Z

Content type: opinion

Language: en

Sources: [Cockroach Labs](<https://devfeed.tech/sources/cockroach-labs.md>)

Topics: [incident](<https://devfeed.tech/topics/incident.md>), [Cockroach Labs](<https://devfeed.tech/topics/cockroach-labs.md>), [distributed-systems](<https://devfeed.tech/topics/distributed-systems.md>), [CockroachDB](<https://devfeed.tech/topics/cockroachdb.md>), [Latency](<https://devfeed.tech/topics/latency.md>), [Resilience](<https://devfeed.tech/topics/resilience.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Amazon SageMaker](<https://devfeed.tech/topics/amazon-sagemaker.md>), [Amazon Bedrock](<https://devfeed.tech/topics/amazon-bedrock.md>), [Embeddings](<https://devfeed.tech/topics/embeddings.md>), [event driven](<https://devfeed.tech/topics/event-driven.md>), [Transactions](<https://devfeed.tech/topics/transactions.md>)

Tags: [aws](<https://devfeed.tech/tags/aws.md>), [azure](<https://devfeed.tech/tags/azure.md>), [cockroachdb](<https://devfeed.tech/tags/cockroachdb.md>), [embeddings](<https://devfeed.tech/tags/embeddings.md>), [event-driven](<https://devfeed.tech/tags/event-driven.md>), [incident](<https://devfeed.tech/tags/incident.md>), [latency](<https://devfeed.tech/tags/latency.md>), [microsoft](<https://devfeed.tech/tags/microsoft.md>), [outages](<https://devfeed.tech/tags/outages.md>), [real-time](<https://devfeed.tech/tags/real-time.md>), [resilience](<https://devfeed.tech/tags/resilience.md>), [sql](<https://devfeed.tech/tags/sql.md>), [transactions](<https://devfeed.tech/tags/transactions.md>)

### AI overview

The article argues that fraud defenses should remain resilient during cloud-provider and regional outages. It presents CockroachDB with AWS AI services, including Amazon SageMaker and Amazon Bedrock, as components of a globally distributed, low-latency, event-driven fraud decisioning architecture.

### Source excerpt

Two major cloud outages in two weeks made one thing painfully clear: your fraud defenses can't depend on any single region or provider being perfect all the time. Microsoft's Azure Front Door misconfiguration rippled through widely used services and status systems, just days after a separate AWS incident disrupted thousands of apps globally. These weren't niche blips--they were broad shocks to the digital economy.

## 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.

## Deploy models on AWS Inferentia2 from Hugging Face

DevFeed: [Deploy models on AWS Inferentia2 from Hugging Face](<https://devfeed.tech/articles/deploy-models-on-aws-inferentia2-from-hugging-face-7285.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/inferentia-inference-endpoints>)

Author: Jeff Boudier; Philipp Schmid

Published: 2024-05-22T00:00:00Z

Content type: release

Language: en

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

Topics: [AWS AI chips](<https://devfeed.tech/topics/aws-ai-chips.md>), [inference-endpoints](<https://devfeed.tech/topics/inference-endpoints.md>), [hugging face](<https://devfeed.tech/topics/hugging-face.md>), [Amazon SageMaker](<https://devfeed.tech/topics/amazon-sagemaker.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [tgi](<https://devfeed.tech/topics/tgi.md>), [autoscaling](<https://devfeed.tech/topics/autoscaling.md>), [Amazon Web Services](<https://devfeed.tech/topics/aws.md>), [MLOps](<https://devfeed.tech/topics/mlops.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>)

Tags: [autoscaling](<https://devfeed.tech/tags/autoscaling.md>), [aws](<https://devfeed.tech/tags/aws.md>), [aws-trainium](<https://devfeed.tech/tags/aws-trainium.md>), [cloud](<https://devfeed.tech/tags/cloud.md>), [deployment](<https://devfeed.tech/tags/deployment.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>), [large-language-models-llms](<https://devfeed.tech/tags/large-language-models-llms.md>), [llama3](<https://devfeed.tech/tags/llama3.md>), [mlops](<https://devfeed.tech/tags/mlops.md>), [model](<https://devfeed.tech/tags/model.md>), [optimum](<https://devfeed.tech/tags/optimum.md>), [partnership](<https://devfeed.tech/tags/partnership.md>), [tgi](<https://devfeed.tech/tags/tgi.md>)

### AI overview

Hugging Face announces support for deploying models on AWS Inferentia2 through Amazon SageMaker and Hugging Face Inference Endpoints. The update enables scalable inference for supported models, including Meta Llama 3, with multiple instance sizes, managed features, and autoscaling.

### Source excerpt

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