AWS Machine Learning Blog
Category: AWS Professional Services
AWS performs fine-tuning on a Large Language Model (LLM) to classify toxic speech for a large gaming company
The video gaming industry has an estimated user base of over 3 billion worldwide1. It consists of massive amounts of players virtually interacting with each other every single day. Unfortunately, as in the real world, not all players communicate appropriately and respectfully. In an effort to create and maintain a socially responsible gaming environment, AWS […]
Face-off Probability, part of NHL Edge IQ: Predicting face-off winners in real time during televised games
Face-off Probability is the National Hockey League’s (NHL) first advanced statistic using machine learning (ML) and artificial intelligence. It uses real-time Player and Puck Tracking (PPT) data to show viewers which player is likely to win a face-off before the puck is dropped, and provides broadcasters and viewers the opportunity to dive deeper into the […]
Create high-quality data for ML models with Amazon SageMaker Ground Truth
Machine learning (ML) has improved business across industries in recent years—from the recommendation system on your Prime Video account, to document summarization and efficient search with Alexa’s voice assistance. However, the question remains of how to incorporate this technology into your business. Unlike traditional rule-based methods, ML automatically infers patterns from data so as to […]
Optimize F1 aerodynamic geometries via Design of Experiments and machine learning
FORMULA 1 (F1) cars are the fastest regulated road-course racing vehicles in the world. Although these open-wheel automobiles are only 20–30 kilometers (or 12–18 miles) per-hour faster than top-of-the-line sports cars, they can speed around corners up to five times as fast due to the powerful aerodynamic downforce they create. Downforce is the vertical force […]
Part 4: How NatWest Group migrated ML models to Amazon SageMaker architectures
The adoption of AWS cloud technology at NatWest Group means moving our machine learning (ML) workloads to a more robust and scalable solution, while reducing our time-to-live to deliver the best products and services for our customers. In this cloud adoption journey, we selected the Customer Lifetime Value (CLV) model to migrate to AWS. The […]
Part 2: How NatWest Group built a secure, compliant, self-service MLOps platform using AWS Service Catalog and Amazon SageMaker
This is the second post of a four-part series detailing how NatWest Group, a major financial services institution, partnered with AWS Professional Services to build a new machine learning operations (MLOps) platform. In this post, we share how the NatWest Group utilized AWS to enable the self-service deployment of their standardized, secure, and compliant MLOps […]
How Intel Olympic Technology Group built a smart coaching SaaS application by deploying pose estimation models – Part 1
February 9, 2024: Amazon Kinesis Data Firehose has been renamed to Amazon Data Firehose. Read the AWS What’s New post to learn more. The Intel Olympic Technology Group (OTG), a division within Intel focused on bringing cutting-edge technology to Olympic athletes, collaborated with AWS Machine Learning Professional Services (MLPS) to build a smart coaching software […]
Accelerating the deployment of PPE detection solution to comply with safety guidelines
Personal protective equipment (PPE) such as face covers (face mask), hand covers (gloves), and head covers (helmet) are essential for many businesses. For example, helmets are required at construction sites for employee safety, and gloves and face masks are required in the restaurant industry for hygienic operations. In the current COVID-19 pandemic environment, PPE compliance […]