Machine Learning with Spark - Second Edition

Model monitoring and feedback

It is critically important to monitor the performance of our machine learning system in production. Once we deploy our optimal-trained model, we wish to understand how it is doing in the "wild". Is it performing as we expect on new, unseen data? Is its accuracy good enough? The reality is, regardless of how much model selection and tuning we try to do in the earlier phases, the only way to measure true performance is to observe what happens in our production system.

In addition to the batch mode model creation, there are also models built with Spark streaming which are real-time in nature.

Also, bear in mind that model accuracy and predictive performance is only one aspect of a real-world system. Usually, we ...