AWS Machine Learning Blog

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, […]

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 […]

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, […]

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 […]

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 […]

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 […]

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 […]

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 […]

This post is a guest joint collaboration by Supratim Banerjee of More Retail Limited and Shivaprasad KT and Gaurav H Kankaria of Ganit Inc. More Retail Ltd. (MRL) is one of India’s top four grocery retailers, with a revenue in the order of several billion dollars. It has a store network of 22 hypermarkets and […]

This blog is co-authored by Sarah Jane Hong CSO, Darryl Barnhart CTO, and Ian Thompson CEO of Latent Space and Prem Ranga of AWS. Latent space is a hidden representation of abstract ideas that machine learning (ML) models learn. For example, “dog,” “flower,” or “door” are concepts or locations in latent space. At Latent Space, […]