Chapter 10. Real-Time Machine Learning

In the previous chapters, we ingested historical flight data and used it to train a machine learning model capable of predicting whether a flight will be late. We deployed the trained model and demonstrated that we could get the prediction for an individual flight by sending input variables to the model in the form of a REST call.

The input variables to the model include information about the flight whose on-time performance is desired. Most of these variables—the departure delay of the flight, the distance of the flight, and the time it takes to taxi out to the runway—are specific to the flight itself. However, the inputs to the machine learning model also included two time aggregates—the historic departure delay at the specific departure airport and the current arrival delay at the flight’s destination—that require more effort to compute. In Chapter 8, we wrote an Apache Beam pipeline to compute these averages on the training dataset so as to be able to train the machine learning model. In Chapter 9, we trained a TensorFlow model capable of using the input variables to predict whether the flight would be late. We were also able to deploy this model on Google Cloud Platform as a web service and invoke the service to make predictions.

In this chapter, we build a real-time Beam pipeline that takes each flight and adds the predicted on-time performance of the flight and writes it out to a database. The resulting table can then be queried by ...

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