AnacondaCon 2018. Justin Brandenburg. The idea behind predictive maintenance is that the failure patterns of various types of equipment are predictable. If an organization can accurately predict when a piece of hardware will fail, and replace that component before it fails, it can achieve much higher levels of operational efficiency. With many devices now including sensor data and other components that send diagnosis reports, predictive maintenance using big data is increasingly accurate and effective. In this case, how can we enhance our data monitoring to predict the next event? This talk will present an actual use case in the IoT industry 4.0 space. Justin will present an entire workflow of data ingestion, bulk ETL, data exploration, model training, testing, and deployment in a real time streaming architecture that can scale. He will demonstrate how he used Anaconda Python 3.5 and Pyspark 2.1.0 to wrangle data and train a recurrent neural network to predict whether the next event in a real time stream indicated that maintenance was required.
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