AWS Machine Learning Blog

Category: Artificial Intelligence

Amazon Kendra adds new search connectors from AWS Partner, Perficient, to help customers search enterprise content faster

Today, Amazon Kendra is making nine new search connectors available in the Amazon Kendra connector library developed by Perficient, an AWS Partner. These include search connectors for IBM Case Manager, Adobe Experience Manger, Atlassian Jira and Confluence, and many others. Improving the Enterprise Search Experience These days, employees and customers expect an intuitive search experience. […]

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Enable feature reuse across accounts and teams using Amazon SageMaker Feature Store

Amazon SageMaker Feature Store is a new capability of Amazon SageMaker that helps data scientists and machine learning (ML) engineers securely store, discover, and share curated data used in training and prediction workflows. As organizations build data-driven applications using ML, they’re constantly assembling and moving features between more and more functional teams. This constant movement […]

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AWS and Hugging Face collaborate to simplify and accelerate adoption of Natural Language Processing models

Just like computer vision a few years ago, the decade-old field of natural language processing (NLP) is experiencing a fascinating renaissance. Not a month goes by without a new breakthrough! Indeed, thanks to the scalability and cost-efficiency of cloud-based infrastructure, researchers are finally able to train complex deep learning models on very large text datasets, […]

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Announcing AWS Media Intelligence Solutions

Today, we’re pleased to announce the availability of AWS Media Intelligence (AWS MI) solutions, a combination of services that empower you to easily integrate AI into your media content workflows. AWS MI allows you to analyze your media, improve content engagement rates, reduce operational costs, and increase the lifetime value of media content. With AWS […]

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Create forecasting systems faster with automated workflows and notifications in Amazon Forecast

You can now enable notifications for workflow status changes while using Amazon Forecast, allowing you to work seamlessly without the disruption of having to check if a particular workflow has completed. Additionally, you can now automate workflows through the notifications to increase work efficiency. Forecast uses machine learning (ML) to generate more accurate demand forecasts, […]

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RAPIDS and Amazon SageMaker: Scale up and scale out to tackle ML challenges

In this post, we combine the powers of NVIDIA RAPIDS and Amazon SageMaker to accelerate hyperparameter optimization (HPO). HPO runs many training jobs on your dataset using different settings to find the best-performing model configuration. HPO helps data scientists reach top performance, and is applied when models go into production, or to periodically refresh deployed […]

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The following images show an example (left) where the model predicted every helmet correctly

Helmet detection error analysis in football videos using Amazon SageMaker

The National Football League (NFL) is America’s most popular sports league. Founded in 1920, the NFL developed the model for the successful modern sports league and is committed to advancing progress in the diagnosis, prevention, and treatment of sports-related injuries. Health and safety efforts include support for independent medical research and engineering advancements in addition […]

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Explaining Bundesliga Match Facts xGoals using Amazon SageMaker Clarify

One of the most exciting AWS re:Invent 2020 announcements was a new Amazon SageMaker feature, purpose built to help detect bias in machine learning (ML) models and explain model predictions: Amazon SageMaker Clarify. In today’s world where predictions are made by ML algorithms at scale, it’s increasingly important for large tech organizations to be able […]

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AI for AgriTech: Classifying Kiwifruits using Amazon Rekognition Custom Labels

Computer vision is a field of artificial intelligence (AI) that is gaining in popularity and interest largely due to increased access to affordable cloud-based training compute, more performant algorithms, and optimizations for scalable model deployment and inference. However, despite these advances in individual AI and machine learning (ML) domains, simplifying ML pipelines into coherent and […]

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Perform interactive data processing using Spark in Amazon SageMaker Studio Notebooks

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 Studio notebooks to explore datasets and build models. You can now use Studio notebooks to securely connect to Amazon EMR clusters and prepare vast amounts of data for […]

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