Industries updates

With the advent of generative AI solutions, organizations are finding different ways to apply these technologies to gain edge over their competitors. Intelligent applications, powered by advanced foundation models (FMs) trained on huge datasets, can now understand natural language, interpret meaning and intent, and generate contextually relevant and human-like responses. This is fueling innovation across …

Build generative AI chatbots using prompt engineering with Amazon Redshift and Amazon Bedrock Read More »

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 BigBasket improved AI-enabled checkout at their physical stores using Amazon SageMaker Read More »

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 …

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

Google Pixel and National Women’s Soccer League announce a multi-year partnership, with Pixel becoming the league’s official mobile phone.

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 …

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

Enterprises are seeking to quickly unlock the potential of generative AI by providing access to foundation models (FMs) to different lines of business (LOBs). IT teams are responsible for helping the LOB innovate with speed and agility while providing centralized governance and observability. For example, they may need to track the usage of FMs across …

Build an internal SaaS service with cost and usage tracking for foundation models on Amazon Bedrock Read More »

Generative AI agents are a versatile and powerful tool for large enterprises. They can enhance operational efficiency, customer service, and decision-making while reducing costs and enabling innovation. These agents excel at automating a wide range of routine and repetitive tasks, such as data entry, customer support inquiries, and content generation. Moreover, they can orchestrate complex, …

Automate the insurance claim lifecycle using Agents and Knowledge Bases for Amazon Bedrock Read More »

Bard is now known as Gemini, with a mobile app and an Advanced experience that gives you access to our most capable AI model, Ultra 1.0.

We’re bringing Gemini to more Google products. Bard will now be called Gemini, and you can access our most capable AI model, Ultra 1.0, in Gemini Advanced.

In the first post of this three-part series, we presented a solution that demonstrates how you can automate detecting document tampering and fraud at scale using AWS AI and machine learning (ML) services for a mortgage underwriting use case. In the second post, we discussed an approach to develop a deep learning-based computer vision model …

Automate mortgage document fraud detection using an ML model and business-defined rules with Amazon Fraud Detector: Part 3 Read More »