Predictive maintenance is one of the most common machine learning use cases and with the latest advancements in information technology, the volume of stored data is growing faster in this domain than ever before which makes it necessary to leverage big data analytic capabilities to efficiently transform large amounts of data into business intelligence.
Microsoft has published a series of learning materials including blogs, solution templates, modeling guides and sample tutorials in the domain of predictive maintenance. Recently, we extended those materials by providing a detailed step-by-step tutorial of using Spark Python API PySpark to demonstrate how to approach predictive maintenance for big data scenarios. The tutorial covers typical data science steps such as data ingestion, cleansing, feature engineering and model development.
Read about it on the Azure blog.