Category: Amazon ML Solutions Lab

At AWS re:Invent 2020, AWS released the profiling functionality for Amazon SageMaker Debugger. In this post, we expand on the importance of profiling deep neural network (DNN) training, review some of the common performance bottlenecks you might encounter, and demonstrate how to use the profiling feature in Debugger to detect such bottlenecks. In the context […]

NFL’s Next Gen Stats (NGS) powered by AWS accurately captures player and ball data in real time for every play and every NFL game—over 300 million data points per season—through the extensive use of sensors in players’ pads and the ball. With this rich set of tracking data, NGS uses AWS machine learning (ML) technology […]

With the 2020 football season kicking off, Amazon Web Services (AWS) is continuing its work with the National Football League (NFL) on several ongoing game-changing initiatives. Specifically, the NFL and AWS are teaming up to develop state-of-the-art cloud technology using machine learning (ML) aimed at aiding the officiating process through real-time football detection. As a […]

University of Illinois, Urbana Champaign (UIUC) has partnered with the Amazon Machine Learning Solutions Lab to help UIUC football coaches prepare for games more efficiently and improve their odds of winning. Previously, coaches prepared for games by creating a game planning sheet that only featured types of plays for a certain down and distance, and […]

Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly. Amazon SageMaker removes the heavy lifting from each step of the ML process to make it easier to develop high-quality models. As datasets continue to increase in size, […]

There are many techniques to train deep learning models with a small amount of data. Examples include transfer learning, few-shot learning, or even one-shot learning for an image classification task and fine-tuning for language models based on a pre-trained BERT or GPT2 model. However, you may still have a use case in which you need […]

One of AWS’s goals is to put machine learning (ML) in the hands of every developer. With the open-source AutoML library AutoGluon, deployed using Amazon SageMaker and AWS Lambda, we can take this a step further, putting ML in the hands of anyone who wants to make predictions based on data—no prior programming or data […]

When you build models with the Apache MXNet deep learning framework, you can take advantage of the expansive model zoo provided by GluonCV to quickly train state-of-the-art computer vision algorithms for image and video processing. A typical development environment for training consists of a Jupyter notebook hosted on a compute instance configured by the operating […]

We are excited to announce the Amazon ML Solutions Lab, a new program that connects machine learning experts from across Amazon with AWS customers to help identify novel uses of machine learning inside customers’ businesses, and guide them in developing new machine learning-enabled features, products, and processes. Amazon has been investing in machine learning for more than 20 years, innovating in areas such as fulfilment and logistics, personalization and recommendations, forecasting, fraud prevention, and supply chain optimization. The Amazon ML Solutions Lab provides you access to the same talent that built many of Amazon’s machine learning-powered products and services.