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GTC ON-DEMAND

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Abstract:
Deploying machine learning-based predictive models to the oil field is quite challenging. They are remote, hazardous, and have spotty connectivity to the cloud. The world of operationalizing a model is very different than the perfect lab environment where the models are born. We'll detail the requirements of our oil and gas customers and how we were able to meet those requirements such that we could deploy a new generation of analytics with a complete software engineering discipline and mentality around it by taking advantage of the Microsoft IoT Edge platform. This is currently a pilot project under way and, due to the engineering principals in place, we are able to complete a loop from the field to the lab and back again.
Deploying machine learning-based predictive models to the oil field is quite challenging. They are remote, hazardous, and have spotty connectivity to the cloud. The world of operationalizing a model is very different than the perfect lab environment where the models are born. We'll detail the requirements of our oil and gas customers and how we were able to meet those requirements such that we could deploy a new generation of analytics with a complete software engineering discipline and mentality around it by taking advantage of the Microsoft IoT Edge platform. This is currently a pilot project under way and, due to the engineering principals in place, we are able to complete a loop from the field to the lab and back again.  Back
 
Topics:
AI Application Deployment and Inference, IoT, Robotics & Drones, Robotics & Autonomous Machines
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8714
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