This post is the second part in a series where we will build a real-time example for analysis and monitoring of Uber car GPS trip data. If you have not already read the first part of this series, you should read that first. The first post discussed creating a machine learning model using Apache Spark's K-means algorithm to cluster Uber data based on location. This second post will discuss using the saved K-means model with streaming data to do real-time analysis of where and when Uber cars are clustered. The example data set is Uber trip data, which you can read more about in part 1 of this series.

Data Discovery: The first phase involves analysis on historical data to build the machine learning model. Analytics Using the Model: The second phase uses the model in production on live events. Data Discovery: The first phase involves analysis on historical data to build the machine learning model. Analytics Using the Model: The second phase uses the model in production on live events. In this first post, I'll help you get started using Apache Spark's machine learning K-means algorithm to cluster Uber data based on location.