AWS Machine Learning Blog

Category: Foundational (100)

Boost your content editing with Contentful and Amazon Bedrock

This post is co-written with Matt Middleton from Contentful. Today, jointly with Contentful, we are announcing the launch of the AI Content Generator powered by Amazon Bedrock. The AI Content Generator powered by Amazon Bedrock is an app available on the Contentful Marketplace that allows users to create, rewrite, summarize, and translate content using cutting-edge […]

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 […]

Driving advanced analytics outcomes at scale using Amazon SageMaker powered PwC’s Machine Learning Ops Accelerator

This post was written in collaboration with Ankur Goyal and Karthikeyan Chokappa from PwC Australia’s Cloud & Digital business. Artificial intelligence (AI) and machine learning (ML) are becoming an integral part of systems and processes, enabling decisions in real time, thereby driving top and bottom-line improvements across organizations. However, putting an ML model into production […]

Reduce model deployment costs by 50% on average using the latest features of Amazon SageMaker

As organizations deploy models to production, they are constantly looking for ways to optimize the performance of their foundation models (FMs) running on the latest accelerators, such as AWS Inferentia and GPUs, so they can reduce their costs and decrease response latency to provide the best experience to end-users. However, some FMs don’t fully utilize […]

Use foundation models to improve model accuracy with Amazon SageMaker

Determining the value of housing is a classic example of using machine learning (ML). In this post, we discuss the use of an open-source model specifically designed for the task of visual question answering (VQA). With VQA, you can ask a question of a photo using natural language and receive an answer to your question—also in plain language. Our goal in this post is to inspire and demonstrate what is possible using this technology.

Customize Amazon Textract with business-specific documents using Custom Queries

Amazon Textract is a machine learning (ML) service that automatically extracts text, handwriting, and data from scanned documents. Queries is a feature that enables you to extract specific pieces of information from varying, complex documents using natural language. Custom Queries provides a way for you to customize the Queries feature for your business-specific, non-standard documents […]

Prepare your data for Amazon Personalize with Amazon SageMaker Data Wrangler

A recommendation engine is only as good as the data used to prepare it. Transforming raw data into a format that is suitable for a model is key to getting better personalized recommendations for end-users. In this post, we walk through how to prepare and import the MovieLens dataset, a dataset prepared by GroupLens research […]

A diagram of the customer's architecture

MDaudit uses AI to improve revenue outcomes for healthcare customers

MDaudit provides a cloud-based billing compliance and revenue integrity software as a service (SaaS) platform to more than 70,000 healthcare providers and 1,500 healthcare facilities, ensuring healthcare customers maintain regulatory compliance and retain revenue. Working with the top 60+ US healthcare networks, MDaudit needs to be able to scale its artificial intelligence (AI) capabilities to […]

Accelerate business outcomes with 70% performance improvements to data processing, training, and inference with Amazon SageMaker Canvas

Amazon SageMaker Canvas is a visual interface that enables business analysts to generate accurate machine learning (ML) predictions on their own, without requiring any ML experience or having to write a single line of code. SageMaker Canvas’s intuitive user interface lets business analysts browse and access disparate data sources in the cloud or on premises, […]

Use Stable Diffusion XL with Amazon SageMaker JumpStart in Amazon SageMaker Studio

Today we are excited to announce that Stable Diffusion XL 1.0 (SDXL 1.0) is available for customers through Amazon SageMaker JumpStart. SDXL 1.0 is the latest image generation model from Stability AI. SDXL 1.0 enhancements include native 1024-pixel image generation at a variety of aspect ratios. It’s designed for professional use, and calibrated for high-resolution […]