AWS introduced the Machine Learning (ML) Solutions Lab a little over two years ago to connect our machine learning experts and data scientists with AWS customers. Our goal was to help our customers solve their most pressing business problems using ML. We’ve helped our customers increase fraud detection rates, improved forecasting and predictions for more efficient operations, drive additional revenues through personalization, and even help organizations scale their response to crisis like human trafficking.
Since we began, we’ve successfully assisted a wide array of customers across a diverse spectrum of industries including retail, healthcare, energy, public sector and sports to create new machine learning-powered solutions. Due to the growing demand from customers eager to adopt machine learning around the world, we’ve significantly increased the capacity of the ML Solutions Lab program and expanded from North America to Asia, Australia and Europe.
In our partnership with the National Football League (NFL), we’ve identified and built an entirely new way for fans to engage with the sport through Next Gen Stats, which features stats such as Completion Probability, 3rd Down Conversion Probability, Expected Yards After Catch, Win Probability, and Catch Prediction. Today, Next Gen Stats are an important part of how fans experience the game. And recently we’ve kicked off a new initiative with the NFL to tackle the next challenge—predicting and limiting player injury. As part of this initiative we’re working with the NFL to develop the “Digital Athlete”, a virtual representation of a composite NFL player which will enable us to eventually predict injury and recovery trajectories
In healthcare, we’re working with Cerner, the world’s largest publicly traded healthcare IT company, to apply machine learning-driven solutions to its mission of improving the health of individuals and populations while reducing costs and increasing clinician satisfaction. One important area of this project is using health prediction capabilities to uncover important and potentially life-saving insights within trusted-source, digital health data. For example, using Amazon SageMaker we built a solution to enable researchers to query anonymized patient data to build complex models and algorithms that predicts congestive heart failure up to 15 months before clinical manifestation. And Cerner will also be using AWS AI services such as the newly introduced Amazon Transcribe Medical to free up physicians from tasks such as writing down notes through a virtual scribe.
In the public sector, we collaborated with the NASA Heliophysics Lab to better understand solar super storms. We brought together the expert scientists at NASA with the machine learning experts in the ML Solutions Lab and the AWS Professional Services organizations to improve the ability to predict and categorize solar super storms. With Amazon SageMaker, NASA is using unsupervised learning and anomaly detection to explore the extreme conditions associated with super storms. Such space weather can create radiation hazards for astronauts, cause upsets in satellite electronics, interfere with airplane and shipping radio communications, and damage electric power grids on the ground – making prediction and early warnings critical.
World Kinect Energy Services, a global leader in energy management, fuel supply, and sustainability, turned to the ML Solutions Lab to improve their ability to anticipate the impact of weather changes on energy prices. An important piece of their business model involves trading financial contracts derived from energy prices. This requires an accurate forecast of the energy price. To improve and automate the process of forecasting—historically done manually—we collaborated with them to develop a model using Amazon SageMaker to predict the upcoming weather trends and therefore the prices of future months’ electricity, enabling unprecedented long-range energy trading. By using a deep learning forecasting model to replace the old manual process, World Kinect Energy Services improves their hedging strategy. With the current results, the team is now adding additional signals focused on trend and volatility and is on its way to realizing an accuracy of greater than 60% over the manual process.
And in manufacturing, we worked with Formosa Plastics, one of Taiwan’s top petrochemical companies and a leading plastics manufacturers, to apply ML to more accurately detect defects and reduce manual labor costs. Formosa Plastics needed to ensure the highest Silicon Wafer quality, but the defect inspection process was time consuming and required time from a highly experienced engineers.
Together with the ML Solutions Lab, Fromosa Plastics created and deployed a model using Amazon SageMaker to automatically detect defects. The model reduced their employee time spent doing manual inspection in half and increased the accuracy of detection.
Energized by the progress our customers have made and listening closely to their needs, we continue to increase our capacity to support even more customers seeking Amazon’s expertise in ML. And we recently introduced a new program, AWS Machine Learning Embark program, which combines workshops, on-site training, an ML Solutions Lab engagement, and an AWS DeepRacer event to help organizations fast-track their adoption of ML.
We started the ML Solutions Lab because we believe machine learning has the ability to transform every industry, process, and business, but the path to machine learning success is not always straightforward. Many organizations need a partner to help them along their journey. Amazon has been investing in machine learning for more than 20 years, innovating in areas such as fulfilment and logistics, personalization and recommendations, forecasting, robotics and automation, fraud prevention, and supply chain optimization. We bring the learnings from this experience to every customer engagement. We’re excited to be a part of our customers’ adoption of this transformational technology, and we look forward to another year of working hand in hand with our customers to find the most impactful ML use cases for their organization. To learn more about the AWS Machine Learning Solutions Lab contact your account manager or visit us at https://aws.amazon.com/ml-solutions-lab/.
About the Author
Michelle K. Lee, Vice President of the Machine Learning Solutions Lab, AWS