Why machine learning models fail

by Jeremy

Machine Learning is quickly becoming an essential tool for automation, but failing models and improper background knowledge create more issues than solving. 

“I think to build a good machine learning model… if you’re trying to do it repeatedly, you need great talent; you need an outstanding research process. Then finally you need technology and tooling that’s kind of up to date and modern,” said Matthew Granade, co-founder of machine learning platform provider Domino Data Lab. He explained how all three of these elements must come together and operate in unity to create the best possible model. However, Granade placed a particular emphasis on the second aspect. “The research process determines how you’re going to identify problems to work on, find data, work with other parts of the business, test your results, and deliver those results to the business,” he explained. 

8 11 machine learning fail

According to Granade, the absence of the essential combination of those aspects is why so many organizations face failing models. “Companies have high expectations for what data science can do, but they’re struggling to bring those three different ingredients together,” he said. This raises the question: why are organizations investing so much into machine learning models but failing to invest in the things that will make their models an ultimate success? According to a study conducted by Domino Data Lab, 97% of those polled say data science is crucial to long-term success. However, many say that organizations lack the staff, skills, and tools needed to sustain that success. 

Granade traces this problem back to the tendency to look for shortcuts. “I think the mistake a lot of companies make is that they kind of look for a quick fix,” he began, “They look for a point solution or this idea of ‘I’m going to hire three or four smart PHDs, and that’s going to solve my problem.’ ’’ According to Granade, these types of quick fixes never work long-term because the issues run deeper. It will always be essential to have the best minds on your team, but they cannot exist independently. Without the best processes and tech to back them up, it becomes futile to utilize data science.

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