Optimize price-performance of LLM inference on NVIDIA GPUs using the Amazon SageMaker integration with NVIDIA NIM Microservices

By Admin 19/03/2024

AWS Machine Learning Blog NVIDIA NIM microservices now integrate with Amazon SageMaker, allowing you to deploy industry-leading large language models (LLMs) and optimize model performance and cost. You can deploy state-of-the-art LLMs in minutes instead of days using technologies such as NVIDIA TensorRT, NVIDIA TensorRT-LLM, and NVIDIA Triton Inference Server on NVIDIA accelerated instances hosted […]Continue reading

Amazon SageMaker Feature Store now supports cross-account sharing, discovery, and access

By Admin 13/02/2024

AWS Machine Learning Blog Amazon SageMaker Feature Store is a fully managed, purpose-built repository to store, share, and manage features for machine learning (ML) models. Features are inputs to ML models used during training and inference. For example, in an application that recommends a music playlist, features could include song ratings, listening duration, and listener […]Continue reading

Announcing support for Llama 2 and Mistral models and streaming responses in Amazon SageMaker Canvas

By Admin 05/02/2024

AWS Machine Learning Blog Launched in 2021, Amazon SageMaker Canvas is a visual, point-and-click service for building and deploying machine learning (ML) models without the need to write any code. Ready-to-use Foundation Models (FMs) available in SageMaker Canvas enable customers to use generative AI for tasks such as content generation and summarization. We are thrilled […]Continue reading

Boost productivity on Amazon SageMaker Studio: Introducing JupyterLab Spaces and generative AI tools

By Admin 15/12/2023

AWS Machine Learning Blog Amazon SageMaker Studio offers a broad set of fully managed integrated development environments (IDEs) for machine learning (ML) development, including JupyterLab, Code Editor based on Code-OSS (Visual Studio Code Open Source), and RStudio. It provides access to the most comprehensive set of tools for each step of ML development, from preparing […]Continue reading

Package and deploy classical ML and LLMs easily with Amazon SageMaker, part 2: Interactive User Experiences in SageMaker Studio

By Admin 01/12/2023

AWS Machine Learning Blog Amazon SageMaker is a fully managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning (ML) models at scale. SageMaker makes it easy to deploy models into production directly through API calls to the service. Models are packaged into containers for robust and […]Continue reading

Package and deploy classical ML and LLMs easily with Amazon SageMaker, part 1: PySDK Improvements

By Admin 01/12/2023

AWS Machine Learning Blog Amazon SageMaker is a fully managed service that enables developers and data scientists to quickly and effortlessly build, train, and deploy machine learning (ML) models at any scale. SageMaker makes it straightforward to deploy models into production directly through API calls to the service. Models are packaged into containers for robust […]Continue reading

Minimize real-time inference latency by using Amazon SageMaker routing strategies

By Admin 30/11/2023

AWS Machine Learning Blog Amazon SageMaker makes it straightforward to deploy machine learning (ML) models for real-time inference and offers a broad selection of ML instances spanning CPUs and accelerators such as AWS Inferentia. As a fully managed service, you can scale your model deployments, minimize inference costs, and manage your models more effectively in […]Continue reading

Schedule Amazon SageMaker notebook jobs and manage multi-step notebook workflows using APIs

By Admin 30/11/2023

AWS Machine Learning Blog Amazon SageMaker Studio provides a fully managed solution for data scientists to interactively build, train, and deploy machine learning (ML) models. Amazon SageMaker notebook jobs allow data scientists to run their notebooks on demand or on a schedule with a few clicks in SageMaker Studio. With this launch, you can programmatically […]Continue reading

Boost inference performance for LLMs with new Amazon SageMaker containers

By Admin 28/11/2023

AWS Machine Learning Blog Today, Amazon SageMaker launches a new version (0.25.0) of Large Model Inference (LMI) Deep Learning Containers (DLCs) and adds support for NVIDIA’s TensorRT-LLM Library. With these upgrades, you can effortlessly access state-of-the-art tooling to optimize large language models (LLMs) on SageMaker and achieve price-performance benefits – Amazon SageMaker LMI TensorRT-LLM DLC […]Continue reading

Optimizing costs for Amazon SageMaker Canvas with automatic shutdown of idle apps

By Admin 24/11/2023

AWS Machine Learning Blog Amazon SageMaker Canvas is a rich, no-code Machine Learning (ML) and Generative AI workspace that has allowed customers all over the world to more easily adopt ML technologies to solve old and new challenges thanks to its visual, no-code interface. It does so by covering the ML workflow end-to-end: whether you’re […]Continue reading