Preventing Failures with Predictive Maintenance Webinar

The science of predicting system failures with Microsoft Azure

Recorded September 2017

What would it mean to your organization if you could predict system failures or quality issues before they happen? Preventing failures can help your organization reduce unscheduled downtime, waste, and rework – and avoid costly disruptions in operations.

With predictive analytics, it’s possible to proactively manage maintenance and improve operational efficiency by discovering the chance of a failure before it takes place. In this past webinar, we discussed how you can utilize Azure Machine Learning, R, and the Cortana Intelligence Suite to predict and prevent system failure, and explored the benefits of calculating KPIs such as Remaining Useful Life, Time to Failure, and Failure within a certain time.

The recording is available below! For more information on Predictive Maintenance, check out our solution offering!

 
 
 
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 Webinar GOALS

  • Discuss use cases for predictive maintenance in manufacturing and equipment operations
  • Learn how to utilize R and Azure Machine Learning for developing useful predictions
  • Demonstrate the benefit of calculating KPIs such as Remaining Useful Life, Time to Failure, and Failure within a certain time
  • Discover the value of applying Azure Event Hub, Stream Analytics, Azure Data Factory, and HDInsight

 Webinar Details

  • Recorded September 2017
  • Content is intended for BI/DW Professionals and Data Scientists

PRESENTER

Andy Lathrop
Principal Consultant 

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Andy is a Principal Consultant at BlueGranite. He is passionate about helping customers employ modern tools as part of the democratization of data, and now, data science. Drawing on a diverse background including military service, non-profit work, and over 13 years in enterprise analytics, Andy loves solving complex business problems that require leadership, teamwork, and technical skills. He has expertise in advanced business analytics using R, SAS, Monte Carlo simulation, discrete-event simulation, Azure ML, Power BI, and Spotfire.

 

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