Category: Amazon SageMaker AI

In this post, we demonstrate how you can deploy an LLM such as DeepSeek-R1—or another FM of your choice—from popular model hubs like SageMaker JumpStart or Hugging Face Hub to SageMaker AI for real-time inference. We explore inference frameworks like Hugging Face TGI which helps streamline deployment while integrating built-in performance optimizations to minimize latency and maximize throughput. Additionally, we showcase how the SageMaker developer-friendly Python SDK simplifies endpoint orchestration, allowing seamless experimentation and scaling of LLM-powered applications.

This post provides detailed steps for setting up the key components of a multi-account ML platform. This includes configuring the ML Shared Services Account, which manages the central templates, model registry, and deployment pipelines; sharing the ML Admin and SageMaker Projects Portfolios from the central Service Catalog; and setting up the individual ML Development Accounts where data scientists can build and train models.

This post walks you through the end-to-end process of deploying a single custom model on SageMaker using NASA’s Prithvi model. The Prithvi model is a first-of-its-kind temporal Vision transformer pre-trained by the IBM and NASA team on contiguous US Harmonised Landsat Sentinel 2 (HLS) data. It can be finetuned for image segmentation using the mmsegmentation library for use cases like burn scars detection, flood mapping, and multi-temporal crop classification.

In this post, we show how to create an automated continuous integration and delivery (CI/CD) pipeline solution to build, scan, and deploy custom Docker images to SageMaker Studio domains. You can use this solution to promote consistency of the analytical environments for data science teams across your enterprise.

In this post, we walk you through the process to build an automated mechanism using Amazon SageMaker to process your log data, run training iterations over it to obtain the best-performing anomaly detection model, and register it with the Amazon SageMaker Model Registry for your customers to use it.