AWS Machine Learning Blog

Category: Learning Levels

Use Amazon Titan models for image generation, editing, and searching

Amazon Bedrock provides a broad range of high-performing foundation models from Amazon and other leading AI companies, including Anthropic, AI21, Meta, Cohere, and Stability AI, and covers a wide range of use cases, including text and image generation, searching, chat, reasoning and acting agents, and more. The new Amazon Titan Image Generator model allows content […]

High Level Retrieval Augmented Generation Architecture

Build a contextual chatbot application using Knowledge Bases for Amazon Bedrock

May 2024: This post was reviewed and updated in to provide the chatbot application’s infrastructure as code using the AWS CDK. Modern chatbots can serve as digital agents, providing a new avenue for delivering 24/7 customer service and support across many industries. Their popularity stems from the ability to respond to customer inquiries in real […]

Two graphs for timeseries. The top shows the timeseries for motor temperatures and motor speeds. The lower graph shows the anomaly score over time with three peaks that indicate anomalies..

Detect anomalies in manufacturing data using Amazon SageMaker Canvas

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 […]

Skeleton-based pose annotation labeling using Amazon SageMaker Ground Truth

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 […]

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

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 […]

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 […]

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

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 […]

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

In this post, we show you how to build an internal SaaS layer to access foundation models with Amazon Bedrock in a multi-tenant (team) architecture. We specifically focus on usage and cost tracking per tenant and also controls such as usage throttling per tenant. We describe how the solution and Amazon Bedrock consumption plans map to the general SaaS journey framework. The code for the solution and an AWS Cloud Development Kit (AWS CDK) template is available in the GitHub repository.

Knowledge Bases overview

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

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

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 […]