Predictive aircraft maintenance: Difference between revisions
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Client: Adam Durant, [[Satavia]] - <adam.durant@satavia.com> | |||
Local company Satavia helps airlines to schedule engine maintenance based on the amount of exposure the components have had to air pollution, dust, volcanic eruptions and other factors. They have large data sets which could be used to train predictive models that might be added to the Microsoft Cortana Intelligence Solution Template Playbook for predictive maintenance in aerospace. You will need to deliver a data ingestion architecture for a range of global data, and also demonstrate an aircraft maintenance scheduling application that applies the results. | Local company Satavia helps airlines to schedule engine maintenance based on the amount of exposure the components have had to air pollution, dust, volcanic eruptions and other factors. They have large data sets which could be used to train predictive models that might be added to the Microsoft Cortana Intelligence Solution Template Playbook for predictive maintenance in aerospace. You will need to deliver a data ingestion architecture for a range of global data, and also demonstrate an aircraft maintenance scheduling application that applies the results. | ||
Revision as of 17:00, 2 November 2017
Client: Adam Durant, Satavia - <adam.durant@satavia.com>
Local company Satavia helps airlines to schedule engine maintenance based on the amount of exposure the components have had to air pollution, dust, volcanic eruptions and other factors. They have large data sets which could be used to train predictive models that might be added to the Microsoft Cortana Intelligence Solution Template Playbook for predictive maintenance in aerospace. You will need to deliver a data ingestion architecture for a range of global data, and also demonstrate an aircraft maintenance scheduling application that applies the results.
