In this post, we show how to train robot policies for the Unitree H1 humanoid with NVIDIA Isaac Lab on Amazon SageMaker AI across two compute options: Amazon SageMaker HyperPod and Amazon SageMaker Training Jobs.

Federal agencies will gain access to Amazon SageMaker AI, Amazon Bedrock, Amazon Nova among other AI services from AWS.

Step by step deployment of Gemma 4 E2B to a SageMaker real-time endpoint on one NVIDIA L4 with the AWS vLLM container, driven by the aws CLI and managed by a Python MCP server from Claude Code or Gemini CLI.

In this post, we show how to train robot policies for the Unitree H1 humanoid with NVIDIA Isaac Lab on Amazon SageMaker AI across two compute options: Amazon SageMaker HyperPod and Amazon SageMaker Training Jobs.

Amazon SageMaker AI enables scalable robot reinforcement learning with NVIDIA Isaac Lab through managed HyperPod clusters and Training Jobs, eliminating infrastructure overhead while supporting distributed policy training across GPU instances.

Step by step deployment of Gemma 4 E2B to a SageMaker real-time endpoint on one NVIDIA L4 with the AWS vLLM container, driven by the aws CLI and managed by a Python MCP server from Claude Code or Gemini CLI.

Fine-tune a 1.5B model to write, run, and iterate on Python code using RL. GRPO training on a single SageMaker GPU for under $8. +152% on hard coding tasks.

Positron, Posit’s IDE for data science, now runs on Amazon SageMaker AI. This post shows how a data scientist explores an Amazon Athena table, validates features in R, trains an XGBoost model in Python, deploys a real-time SageMaker AI endpoint, and reports results with Quarto, all in one governed SageMaker Studio Space.

Calling everything SageMaker is confusing – but a new name would have been worse says AWS re:Invent  Amazon has introduced a new generation of SageMaker at the re:Invent conference in Las Vegas, bringing together analytics and AI, though with some…

SkyRLとAmazon SageMaker HyperPodを使用すると、マルチモーダル強化学習のトレーニングを高速化できます。Qwen3-VL-8Bの視覚言語モデルをGroup Relative Policy Optimization（GRPO）でポストトレーニングすることで、視覚迷路をナビゲートする能力を学習できます。SkyRLとSageMaker HyperPodの統合により、長時間のマルチノードトレーニングを安定して実行できます。

We are happy to announce a new preview feature that adds support for Apache Iceberg REST Catalogs, enabling DuckDB users to connect to Amazon S3 Tables and Amazon SageMaker Lakehouse with ease.

Amazon SageMaker HyperPod and Cloud Native Qumulo let you place training compute in one AWS Region while keeping your dataset in another. This post shares the architecture and validation results from a cross-Region training run, where a remote cluster matched a co-located cluster’s throughput after a brief NeuralCache warmup.

Preconditions To use the Amazon SageMaker Lakehouse with DuckDB, you first have to create a S3 Table bucket, a namespace and an actual S3 Table. All those steps are described in my other blog post “Query S3 Tables with DuckDB”, so please make sure yo...

Federal agencies will gain access to Amazon SageMaker AI, Amazon Bedrock, Amazon Nova among other AI services from AWS.

Discover more about what's new at AWS with Amazon SageMaker introduces Scale Down to Zero for AI inference to help customers save costs

Groups Conversations All groups and messages Sign in Machine Learning, Deep Learning and AI     Run Positron on Amazon SageMaker AI for data science workflows 0 views Eyal Estrin unread, 10:28 AM (7 minutes ago)    to https://aws.amazon.com/blogs/machine-learning/run-positron-on-amazon-sagemaker-ai-for-data-science-workflows/ Eyal Estrin Author | Cloud Architect | AWS • Azure • GCP Insights Social: @eyalestrin Connect: https://linktr.ee/eyalestrin Blog: https://security-24-7.com Reply all Reply to author Forward

AWS is launching more capabilities in both Amazon Bedrock and Amazon SageMaker AI to make building custom models easier.