Category: Amazon SageMaker

Amazon SageMaker multi-model endpoints (MMEs) are a fully managed capability of SageMaker inference that allows you to deploy thousands of models on a single endpoint. Previously, MMEs pre-determinedly allocated CPU computing power to models statically regardless the model traffic load, using Multi Model Server (MMS) as its model server. In this post, we discuss a […]

Today, we are excited to announce that Code Llama foundation models, developed by Meta, are available for customers through Amazon SageMaker JumpStart to deploy with one click for running inference. Code Llama is a state-of-the-art large language model (LLM) capable of generating code and natural language about code from both code and natural language prompts. […]

With the use of cloud computing, big data and machine learning (ML) tools like Amazon Athena or Amazon SageMaker have become available and useable by anyone without much effort in creation and maintenance. Industrial companies increasingly look at data analytics and data-driven decision-making to increase resource efficiency across their entire portfolio, from operations to performing […]

Pose estimation is a computer vision technique that detects a set of points on objects (such as people or vehicles) within images or videos. Pose estimation has real-world applications in sports, robotics, security, augmented reality, media and entertainment, medical applications, and more. Pose estimation models are trained on images or videos that are annotated with […]

This post is co-written with Santosh Waddi and Nanda Kishore Thatikonda from BigBasket. BigBasket is India’s largest online food and grocery store. They operate in multiple ecommerce channels such as quick commerce, slotted delivery, and daily subscriptions. You can also buy from their physical stores and vending machines. They offer a large assortment of over […]

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 demographics. Features are used […]

This post is co-written with Kostia Kofman and Jenny Tokar from Booking.com. As a global leader in the online travel industry, Booking.com is always seeking innovative ways to enhance its services and provide customers with tailored and seamless experiences. The Ranking team at Booking.com plays a pivotal role in ensuring that the search and recommendation […]

In this post, we show how to develop an ML-driven solution using Amazon SageMaker for detecting adverse events using the publicly available Adverse Drug Reaction Dataset on Hugging Face. In this solution, we fine-tune a variety of models on Hugging Face that were pre-trained on medical data and use the BioBERT model, which was pre-trained on the Pubmed dataset and performs the best out of those tried.

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 to announce the latest […]

This is a guest post co-authored by Ajay K Gupta, Jean Felipe Teotonio and Paul A Churchyard from HSR.health. HSR.health is a geospatial health risk analytics firm whose vision is that global health challenges are solvable through human ingenuity and the focused and accurate application of data analytics. In this post, we present one approach […]