How Machine Learning Helps Predict Equipment Failure

Presented by

Yaroslav Nedashkovskyi, System Architect at SoftElegance

About this talk

We are going to discuss a case study on a unified data lake for the oil industry -- it is a software architecture and a set of microservices that are used to get business values from the data that are generated during the oil production. Math models were developed to make failure prediction of rod pumps during the oil artificial lifting. We used modern capabilities of Big Data Architecture, based on Apache Spark set of technologies, machine learning, archived data, and streaming data from wells to build a unified math model to predict failure of that kind of industrial equipment. Join this webinar to learn: -- How machine learning can help to predict failure of industrial equipment -- Architecture to handle near real-time data-flow from oil wells

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