Data classifiers are popular tools for analyzing data produced or otherwise collected by large computer networks. Data classification enables a computer network to analyze and react to a large and evolving data set. Data classifiers can process large data sets (e.g., sometimes referred to as “big data”) that are so large and/or complex that manual data analysis is impracticable. For example, a social networking system can run several application services that continuously produce and collect data. Classifiers can be used to identify new correlations, statistics, trends, patterns, or any combination thereof in datasets of the social networking system. For example, data classification can rely on static rules or evolving machine learning models. To complete a data classification experiment involving machine learning models, a computer system may need to extract and transform input data, train and update machine learning models, deliberate and execute machine learning models, compile and/summarize the classification results, test or evaluate the classification results, or any combination thereof. These actions often consume a large amount of computational resources (e.g., memory capacity, processor capacity, and/or network bandwidth) and require data scientists' or developers' involvement to repeatedly configure each operational step from one data classification experiment to another.
The figures depict various embodiments of this disclosure for purposes of illustration only. One skilled in the art will readily recognize from the following discussion that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of embodiments described herein.