AWS Machine Learning Blog

Category: Customer Solutions

Self-Checkout

How BigBasket improved AI-enabled checkout at their physical stores using Amazon SageMaker

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 […]

How Booking.com modernized its ML experimentation framework with Amazon SageMaker

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 […]

Accenture creates a regulatory document authoring solution using AWS generative AI services

This post is co-written with Ilan Geller, Shuyu Yang and Richa Gupta from Accenture. Bringing innovative new pharmaceuticals drugs to market is a long and stringent process. Companies face complex regulations and extensive approval requirements from governing bodies like the US Food and Drug Administration (FDA). A key part of the submission process is authoring […]

Deploy large language models for a healthtech use case on Amazon SageMaker

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.

Zoonotic spillover risk analysis dashboard

How HSR.health is limiting risks of disease spillover from animals to humans using Amazon SageMaker geospatial capabilities

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 […]

How Mendix is transforming customer experiences with generative AI and Amazon Bedrock

This post was co-written with Ricardo Perdigao, Solution Architecture Manager at Mendix, a Siemens business. Mendix, a Siemens business, offers the low-code platform with the vision and execution designed for today’s complex software development challenges. Since 2005, we’ve helped thousands of organizations worldwide reimagine how they develop applications with our platform’s cutting-edge capabilities. Mendix allows […]

Ball position tracking in the cloud with the PGA TOUR

The PGA TOUR continues to enhance the golf experience with real-time data that brings fans closer to the game. To deliver even richer experiences, they are pursuing the development of a next-generation ball position tracking system that automatically tracks the position of the ball on the green. The TOUR currently uses ShotLink powered by CDW, […]

Build an Amazon SageMaker Model Registry approval and promotion workflow with human intervention

This post is co-written with Jayadeep Pabbisetty, Sr. Specialist Data Engineering at Merck, and Prabakaran Mathaiyan, Sr. ML Engineer at Tiger Analytics. The large machine learning (ML) model development lifecycle requires a scalable model release process similar to that of software development. Model developers often work together in developing ML models and require a robust […]

Event Driven MLOps architecture with SageMaker

Modernizing data science lifecycle management with AWS and Wipro

This post was written in collaboration with Bhajandeep Singh and Ajay Vishwakarma from Wipro’s AWS AI/ML Practice. Many organizations have been using a combination of on-premises and open source data science solutions to create and manage machine learning (ML) models. Data science and DevOps teams may face challenges managing these isolated tool stacks and systems. […]