AWS Machine Learning Blog

Category: Artificial Intelligence

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 […]

Code Llama 70B is now available in Amazon SageMaker JumpStart

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. […]

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 […]

Enhance Amazon Connect and Lex with generative AI capabilities

Effective self-service options are becoming increasingly critical for contact centers, but implementing them well presents unique challenges. Amazon Lex provides your Amazon Connect contact center with chatbot functionalities such as automatic speech recognition (ASR) and natural language understanding (NLU) capabilities through voice and text channels. The bot takes natural language speech or text input, recognizes […]

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 […]

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 […]