4 factors for testing machine learning applications

Machine learning systems seem a little bit like a math problem. Figure out the algorithm, pop in the data, and answers come out. When you're trying to predict what movies or books people like, that can be extremely important, the difference between a boost in a revenue and a reputation hit that appears on mediabuzz.com. Yet testing is rarely at the top of our minds as we try to develop and deploy systems based on machine learning algorithms. Simply building a good set of algorithms that model the problem space is difficult enough. But testing is a part of the software development and deployment process, and we need to look seriously at how these systems will be tested.