SageMaker

Organizations are increasingly deploying custom large language models (LLMs) on Amazon SageMaker AI real-time endpoints using their preferred serving framework – such as SGLang, VLLM, or TorchServe – to help …

Today we are excited to announce that nvidia nemotron The 3Nano 30B model with 3B active parameters is now generally available in the Amazon SageMaker JumpStart Model Catalog. You can …

Operating a self-managed MLflow tracking server comes with administrative overhead, including server maintenance and resource scaling. As teams increase their ML usage, managing resources efficiently during peak usage and idle …

Configure your model in Code/serving.properties: To deploy voxtral-mini, use the following code: option.model_id=mistralai/Voxtral-Mini-3B-2507 option.tensor_parallel_degree=1 To deploy voxtral-small, use the following code: option.model_id=mistralai/Voxtral-Small-24B-2507 option.tensor_parallel_degree=4 Open and run Voxtral-vLLM-BYOC-SageMaker.ipynb to deploy your …

Users can conduct machine learning (ML) data experiments in data environments like Snowflake using Snowpark LibraryHowever, tracking these experiments across diverse environments can be challenging due to the difficulty in …

Building custom foundation models requires coordination of multiple assets across the development lifecycle, such as data assets, compute infrastructure, model architectures and frameworks, lineage, and production deployment. Data scientists create …

Today we’re announcing Amazon SageMaker AI with MLflow, which now includes a serverless capability that dynamically manages the provisioning, scaling, and operations of infrastructure for artificial intelligence and machine learning …