# Amazon SageMaker AI AWS fully managed machine learning service for building, training, and deploying AI models. 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]() Original publisher: [Read original article]() Author: Linpo Guo Published: 2026-09-15T16:11:36Z Content type: tutorial Language: en Sources: [Artificial Intelligence]() Topics: [Artificial Intelligence](), [Amazon SageMaker AI](), [Fine-tuning](), [rlvr](), [grpo](), [Inference](), [model-deployment](), [Reinforcement learning](), [SDK](), [Serverless]() Tags: [ai](), [amazon-sagemaker](), [amazon-sagemaker-ai](), [aws](), [compute](), [cost](), [customization](), [expert-400](), [fine-tuning](), [inference](), [jobs](), [reinforcement-learning](), [rlvr](), [sdk](), [technical-how-to]() ### 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. ## Announcing instance preference lists for Amazon SageMaker AI training jobs DevFeed: [Announcing instance preference lists for Amazon SageMaker AI training jobs]() Original publisher: [Read original article]() Author: Kanwaljit Khurmi Published: 2026-09-15T16:01:47Z Content type: release Language: en Sources: [Artificial Intelligence]() Topics: [Amazon SageMaker AI](), [GPU](), [Model Development](), [data-processing]() Tags: [amazon-sagemaker-ai](), [announcements](), [gpu](), [intermediate-200](), [model-development](), [pipelines](), [scheduled](), [training]() ### AI overview Amazon SageMaker AI introduces instance preference lists for training and processing jobs. Users can specify up to five instance types in priority order, and SageMaker AI launches the job on the first option with available capacity, reducing manual retries and capacity monitoring. ### Source excerpt Amazon SageMaker AI now offers instance preference lists for training and processing jobs. Specify an ordered list of up to five instance types, and SageMaker AI automatically launches on the first type with available capacity, eliminating manual retry loops and capacity-watching scripts. ## Govern models with MLflow and Amazon SageMaker AI Model Registry sync: Part 2 DevFeed: [Govern models with MLflow and Amazon SageMaker AI Model Registry sync: Part 2]() Original publisher: [Read original article]() Author: Melanie Li Published: 2026-09-08T17:03:50Z Content type: article Language: en Sources: [Artificial Intelligence]() Topics: [Amazon SageMaker AI](), [AWS IAM](), [CI/CD](), [Command-line interface]() Tags: [amazon-sagemaker-ai](), [announcements](), [aws](), [aws-identity-and-access-management-iam](), [ci-cd](), [intermediate-200](), [model](), [sync]() ### AI overview This article describes cross-account model-governance patterns for managed MLflow and Amazon SageMaker AI Model Registry sync. It covers hub-and-spoke and hybrid topologies, IAM-based roles, and CI/CD deployment of approved models. ### Source excerpt Governing models across accounts is the next step after automatic model registration. This post extends managed MLflow and Amazon SageMaker AI Model Registry sync to two cross-account governance topologies: a hub-and-spoke pattern that centralizes governance with AWS RAM, and a hybrid pattern that keeps development accounts isolated. ## From Hugging Face to Amazon SageMaker Studio in one click DevFeed: [From Hugging Face to Amazon SageMaker Studio in one click]() Original publisher: [Read original article]() Author: Hazim Qudah; Naidile Murali; Jeff Boudier; Simon Pagezy; Enrique Hernández Calabrés Published: 2026-07-07T21:15:33Z Content type: release Language: en Sources: [Hugging Face - Blog]() Topics: [Amazon SageMaker AI](), [hugging face](), [Amazon Web Services](), [AWS IAM](), [Deployment](), [Fine-tuning](), [enterprise deployment](), [Serverless]() Tags: [amazon-sagemaker-ai](), [aws](), [cloud](), [data](), [deployment](), [fine-tuning](), [hugging-face](), [iam](), [serverless]() ### AI overview Hugging Face and Amazon SageMaker AI now provide a one-click path from supported model pages to SageMaker Studio. The integration can create a configured Studio environment and preserve model context for customization, fine-tuning, training, experimentation, and endpoint deployment. ### Source excerpt Previously, getting started on SageMaker Studio after discovering a model on Hugging Face required navigating multiple steps between opening Amazon SageMaker AI in the AWS Console, creating a domain, configuring IAM permissions, and sometimes requesting GPU quota. For developers who want to iterate quickly, this friction slows down the path from inspiration to experimentation. The integration creates a more direct path from discovery to enterprise deployment. ## How to deploy and fine-tune DeepSeek models on AWS DevFeed: [How to deploy and fine-tune DeepSeek models on AWS]() Original publisher: [Read original article]() Author: Simon Pagezy; Jeff Boudier; David Corvoysier Published: 2025-01-30T00:00:00Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [deepseek](), [inference-endpoints](), [Amazon Web Services](), [hugging face](), [Artificial Intelligence](), [Open Source](), [Amazon Bedrock](), [Amazon SageMaker AI](), [Inference](), [autoscaling](), [Large Language Model]() Tags: [amazon-bedrock](), [amazon-sagemaker](), [amazon-sagemaker-ai](), [artificial-intelligence](), [autoscaling](), [aws](), [deepseek](), [deployment](), [generative-ai](), [how-to](), [inference](), [inference-endpoints](), [llms](), [models](), [open-source](), [partnerships]() ### 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. ## Introducing Glassdoor's ML Registry: A Centralized Artifact Management Solution DevFeed: [Introducing Glassdoor's ML Registry: A Centralized Artifact Management Solution]() Original publisher: [Read original article]() Author: Rich Papalia Published: 2023-08-30T21:20:29Z Content type: article Language: en Sources: [Glassdoor Engineering]() Topics: [Machine Learning & Artificial Intelligence](), [Open Source](), [Development](), [Amazon SageMaker AI]() Tags: [aws](), [configuration-management](), [feature-store](), [lifecycle](), [machine-learning](), [machine-learning-platform](), [open-source](), [pipelines](), [registry]() ### AI overview Glassdoor describes its newly open-sourced ML Registry, a centralized service for managing machine-learning artifacts and related metadata. The registry is intended to provide a single source of truth and consistent access across teams and applications, integrating with other tools and services. ### Source excerpt Photo by Ryunosuke Kikuno on UnsplashGlassdoor's Machine Learning Engineering and Platform Team As part of Glassdoor's journey to becoming an ML-driven company, we've created an exceptional Machine Learning Engineering and Platform team, (see our blog about building this team here) dedicated to developing the foundational infrastructure for all of our ML initiatives. We've strategically built our ML platform by utilizing a combination of buying, building, and adopting from existing open-source solutions. This approach has allowed us to leverage the best of all worlds. Our team has had the opportunity to build numerous tools from scratch, including data pipelines, human-in-the-loop tools, and more. We've also built on top of existing tools like AWS Sagemaker to incorporate powerful in-house customizations into our feature store. We have more exciting plans in flight, such as building a new recommendations platform, further open-source contributions from our platform, and continuing to build on the foundation which we've established. Today, we'll delve into our newly open-sourced ML Registry. What is an ML Registry? The ML lifecycle encompasses far more than just model development. Once a model is built, many questions arise: Where does it reside? How do we access it? What if updates or versioning are needed? Where can we store the metadata describing the model? And what about other non-model artifacts? How can we efficiently manage all of this? Enter the ML Registry. Glassdoor's ML Registry is a centralized management service for ML artifacts and all related metadata. It serves as the single source of truth for all data pertaining to ML, enabling uniform and reliable access to this data across diverse teams and applications. It seamlessly integrates with other tools and services and provides robust, feature-rich functionality. Choosing Between Buying and Building, What Differentiates Our ML Registry? While several paid and open-source model registries already exist, ## Real-Time Machine Learning at Headspace DevFeed: [Real-Time Machine Learning at Headspace]() Original publisher: [Read original article]() Author: Headspace Published: 2021-08-16T16:53:19Z Content type: article Language: en Sources: [Stories by Headspace on Medium]() Topics: [Machine Learning & Artificial Intelligence](), [real-time](), [Inference](), [recommendations](), [Apache Spark](), [databricks](), [Amazon Simple Queue Service (SQS)](), [Amazon Web Services](), [Amazon SageMaker AI](), [Streaming](), [Orchestration](), [Model Development]() Tags: [android](), [apache-spark](), [aws](), [data](), [databricks](), [headspace-engineering](), [inference](), [machine-learning](), [orchestration](), [real-time](), [recommendations](), [sqs](), [streaming](), [web]() ### AI overview This article describes Headspace's infrastructure for real-time machine learning inference and personalized content recommendations. It explains how the team uses modular Publishing, Receiver, Orchestration, and Serving layers with Apache Spark Structured Streaming on Databricks, AWS SQS, Lambda, and SageMaker. ### Source excerpt Author: Yu Chen, Senior Software Engineer / Co-author: Koyuki Nakamori, Senior Engineering Manager Introduction Data is often most valuable when it can be immediately leveraged to make decisions in the moment, but traditionally consumer data is ingested, transformed, persisted, and sits dormant for lengthy periods of time before being used by machine learning and analytics teams. Headspace's core products are iOS, Android, and web-based apps that focus on improving the health and happiness of its users through mindfulness, meditation, sleep, exercise, and focus content. Machine learning models are core to our user experiences by offering recommendations that engage our users with new relevant, personalized content that builds consistent habits in their lifelong journey. Finding a way to leverage user data to generate real-time insights and decisions means that consumer-facing products like the Headspace app can dramatically shorten the end-to-end user feedback loop: actions that users perform a few seconds, or minutes ago can be incorporated in generating a more relevant, personalized, and context-specific content recommendation for the user. This means our ML models could incorporate dynamic features that update throughout the course of a user's day, or even an individual session: Current session bounce rates for sleep content Semantic embeddings for recent user search terms (if a user recently searched for "preparing for big exam", the ML model can assign more weight to Focus-themed meditations) Users' biometric data (i.e., if step counts and heart rate are increasing over the last 10 minutes, we can recommend Move or Exercise content) The Headspace Machine Learning team architected a solution by decomposing the infrastructure systems into modular Publishing, Receiver, Orchestration, and Serving layers, leveraging Apache Spark Structured Streaming on Databricks, AWS SQS, Lambda, and Sagemaker to deliver real-time inference capabilities for our machine learning mod