Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications

by Chip Huyen

Production having different requirements from research is one of the reasons why successful research projects might not always be used in production. For example, ensembling is a technique popular among the winners of many ML competitions, including the famed $1 million Netflix Prize, and yet it’s not widely used in production. Ensembling combines “multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone.”15 While it can give your ML system a small performance improvement, ensembling tends to make a system too complex to be useful in production, e.g., slower to make predictions or harder to interpret the results.