Track SageMaker Unified Studio project costs with custom tags and AWS CUR | Amazon Web Services
Organizations running machine learning (ML), analytics, and generative AI workloads on Amazon SageMaker Unified Studio domains and projects face a…
Tag: SageMaker
NVIDIA Nemotron 3.5 Lightning now available in Amazon SageMaker JumpStart | Amazon Web Services
By Amazon Web Services Publication Date: 2026-08-17 18:06:00 NVIDIA Nemotron 3.5 Lightning is designed for the fast, specialized model execution…
Tiered KV cache for large LLMs on Amazon SageMaker HyperPod with Curvine | Amazon Web Services
Running large language model (LLM) inference at scale typically forces a KV cache trade-off: you either pay for oversized GPU…
End-to-end encrypted ML inference with Amazon SageMaker AI and FHE | Amazon Web Services
Machine learning (ML) inference often requires processing sensitive data—medical records, proprietary business information, or personal communications. What if you could…
NVIDIA Nemotron 3 Ultra now available on Amazon SageMaker JumpStart | Amazon Web Services
Today, we are excited to announce the day-zero availability of NVIDIA Nemotron 3 Ultra on Amazon SageMaker JumpStart. With this…
Fundamental’s Large Tabular Model NEXUS is now available on Amazon SageMaker JumpStart | Amazon Web Services
Today, we’re announcing support for Fundamental’s NEXUS model on Amazon SageMaker AI. With this launch, you can deploy a foundation…
Improve your agent’s tool-calling accuracy with SFT and DPO on Amazon SageMaker AI | Amazon Web Services
AI agents can autonomously handle complex, multi-step tasks, but their effectiveness depends on calling the right tools to retrieve information…
Comprehensive observability for Amazon SageMaker AI LLM inference: From GPU utilization to LLM quality | Amazon Web Services
Deploying large language models (LLMs) at scale on Amazon SageMaker AI Inference makes observability a critical pillar of any production…
Streamline external access to Amazon SageMaker MLflow using a REST API proxy | Amazon Web Services
Machine learning (ML) teams use MLflow to manage their ML lifecycle effectively. Amazon SageMaker MLflow provides comprehensive ML experiment tracking…
Build a custom portal with embedded Amazon SageMaker AI MLflow Apps | Amazon Web Services
As ML teams grow, embedding Amazon SageMaker AI MLflow Apps into a custom portal requires a scalable approach to access…