Louisiana’s largest health system, Ochsner Health System, used advanced machine learning algorithms to create a predictive model leveraging Epic’s machine learning platform powered by Microsoft Azure to accurately predict patient deterioration hours before an adverse event. This early warning system was tightly integrated into Epic, enabling Ochsner’s Rapid Response team to intervene on patients proactively, rather than reactively. During their 90-day pilot at Ochsner Medical Center, the team successfully reduced codes outside of the ICU by 44%.
Learn how to build your own custom machine learning models, and then train and experiment by using Azure Machine Learning. We show you how to deploy the model to an iPhone.
Presented by: Anusua Trivedi
Bring intelligence to the edge by creating, training, and operationalizing models as Docker containers by using Azure Machine Learning. The containers can then be deployed to IoT Edge devices. In this session, we provide a scenario about the importance of edge intelligence, and an overview of Azure ML and Azure IoT Edge. Then we demonstrate how to use Azure ML to create a model and run it on an IoT Edge device.
Presented by: Ted Way
In this session, you’ll get a Search overview, learn about new performance and scale enhancements, and get a glimpse into how Azure Machine Learning and Cognitive Services are used in customers' content and catalog search experiences.
Presented by: Liam Cavanagh
We take a look at updates to Azure Machine Learning that help you create and deliver powerful AI solutions. We show you how Machine Learning will help you perform data cleaning with AI-assisted data prep, increase the rate of experimentation with agile AI development, deploy models everywhere on any device or service, and monitor and manage your models that are being used in production.
Presented by: Sandhya Vankamamidi
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