AWS Machine Learning Blog
Category: Industries
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 […]
Automate your time series forecasting in Snowflake using Amazon Forecast
This post is a joint collaboration with Andries Engelbrecht and James Sun of Snowflake, Inc. The cloud computing revolution has enabled businesses to capture and retain corporate and organizational data without capacity planning or data retention constraints. Now, with diverse and vast reserves of longitudinal data, companies are increasingly able to find novel and impactful […]
Bundesliga Match Fact Pressure Handling: Evaluating players’ performances in high-pressure situations on AWS
Pressing or pressure in football is a process in which a team seeks to apply stress to the opponent player who possesses the ball. A team applies pressure to limit the time an opposition player has left to make a decision, reduce passing options, and ultimately attempt to turn over ball possession. Although nearly all […]
Bundesliga Match Fact Win Probability: Quantifying the effect of in-game events on winning chances using machine learning on AWS
Ten years from now, the technological fitness of clubs will be a key contributor towards their success. Today we’re already witnessing the potential of technology to revolutionize the understanding of football. xGoals quantifies and allows comparison of goal scoring potential of any shooting situation, while xThreat and EPV models predict the value of any in-game […]
Use RStudio on Amazon SageMaker to create regulatory submissions for the life sciences industry
Pharmaceutical companies seeking approval from regulatory agencies such as the US Food & Drug Administration (FDA) or Japanese Pharmaceuticals and Medical Devices Agency (PMDA) to sell their drugs on the market must submit evidence to prove that their drug is safe and effective for its intended use. A team of physicians, statisticians, chemists, pharmacologists, and […]
Developing advanced machine learning systems at Trumid with the Deep Graph Library for Knowledge Embedding
This is a guest post co-written with Mutisya Ndunda from Trumid. Like many industries, the corporate bond market doesn’t lend itself to a one-size-fits-all approach. It’s vast, liquidity is fragmented, and institutional clients demand solutions tailored to their specific needs. Advances in AI and machine learning (ML) can be employed to improve the customer experience, […]
Build taxonomy-based contextual targeting using AWS Media Intelligence and Hugging Face BERT
As new data privacy regulations like GDPR (General Data Protection Regulation, 2017) have come into effect, customers are under increased pressure to monetize media assets while abiding by the new rules. Monetizing media while respecting privacy regulations requires the ability to automatically extract granular metadata from assets like text, images, video, and audio files at […]
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 […]
Whitepaper: Machine Learning Best Practices in Healthcare and Life Sciences
For customers looking to implement a GxP-compliant environment on AWS for artificial intelligence (AI) and machine learning (ML) systems, we have released a new whitepaper: Machine Learning Best Practices in Healthcare and Life Sciences. This whitepaper provides an overview of security and good ML compliance practices and guidance on building GxP-regulated AI/ML systems using AWS […]
Build a mental health machine learning risk model using Amazon SageMaker Data Wrangler
This post is co-written by Shibangi Saha, Data Scientist, and Graciela Kravtzov, Co-Founder and CTO, of Equilibrium Point. Many individuals are experiencing new symptoms of mental illness, such as stress, anxiety, depression, substance use, and post-traumatic stress disorder (PTSD). According to Kaiser Family Foundation, about half of adults (47%) nationwide have reported negative mental health […]