AWS Machine Learning Blog

Category: Artificial Intelligence

Configuring your Amazon Kendra Confluence Server connector

Many builders and teams on AWS use Confluence as a way of collaborating and sharing information within their teams and across their organizations. These types of workspaces are rich with data and contain sets of knowledge and information that can be a great source of truth to answer organizational questions. Unfortunately, it isn’t always easy […]

Read More

Optimizing costs for machine learning with Amazon SageMaker

Applications based on machine learning (ML) can provide tremendous business value. Using ML, we can solve some of the most complex engineering problems that previously were infeasible. One of the advantages of running ML on the AWS Cloud is that you can continually optimize your workloads and reduce your costs. In this post, we discuss […]

Read More

zomato digitizes menus using Amazon Textract and Amazon SageMaker

This post is co-written by Chiranjeev Ghai, ML Engineer at zomato. zomato is a global food-tech company based in India. Are you the kind of person who has very specific cravings? Maybe when the mood hits, you don’t want just any kind of Indian food—you want Chicken Chettinad with a side of paratha, and nothing […]

Read More

Video streaming and deep learning: Using Amazon Kinesis Video Streams with Deep Java Library

Amazon Kinesis Video Streams allows you to easily ingest video data from connected devices for processing. One of the most effective ways to process this video data is using the power of deep learning. You can create an efficient service infrastructure to run these computations with a Java server, but Java support for deep learning […]

Read More

Bringing real-time machine learning-powered insights to rugby using Amazon SageMaker

The Guinness Six Nations Championship began in 1883 as the Home Nations Championship among England, Ireland, Scotland, and Wales, with the inclusion of France in 1910 and Italy in 2000. It is among the oldest surviving rugby traditions and one of the best-attended sporting events in the world. The COVID-19 outbreak disrupted the end of […]

Read More

Building an NLU-powered search application with Amazon SageMaker and the Amazon OpenSearch Service KNN feature

September 8, 2021: Amazon Elasticsearch Service has been renamed to Amazon OpenSearch Service. See details. This post has also been updated with changes required for SageMaker SDK v2 and an improved notebook experience. The rise of semantic search engines has made ecommerce and retail businesses search easier for its consumers. Search engines powered by natural […]

Read More

Arcanum makes Hungarian heritage accessible with Amazon Rekognition

Arcanum specializes in digitizing Hungarian language content, including newspapers, books, maps, and art. With over 30 years of experience, Arcanum serves more than 30,000 global subscribers with access to Hungarian culture, history, and heritage. Amazon Rekognition Solutions Architects worked with Arcanum to add highly scalable image analysis to Arcanum Digitheca, a free service provided by […]

Read More

Securing Amazon SageMaker Studio connectivity using a private VPC

Amazon SageMaker Studio is the first fully integrated development environment (IDE) for machine learning (ML). With a single click, data scientists and developers can quickly spin up Amazon SageMaker Studio Notebooks for exploring datasets and building models. With the new ability to launch Amazon SageMaker Studio in your Amazon Virtual Private Cloud (Amazon VPC), you […]

Read More

Using Amazon SageMaker inference pipelines with multi-model endpoints

Businesses are increasingly deploying multiple machine learning (ML) models to serve precise and accurate predictions to their consumers. Consider a media company that wants to provide recommendations to its subscribers. The company may want to employ different custom models for recommending different categories of products—such as movies, books, music, and articles. If the company wants […]

Read More

Time series forecasting using unstructured data with Amazon Forecast and the Amazon SageMaker Neural Topic Model

As the volume of unstructured data such as text and voice continues to grow, businesses are increasingly looking for ways to incorporate this data into their time series predictive modeling workflows. One example use case is transcribing calls from call centers to forecast call handle times and improve call volume forecasting. In the retail or […]

Read More