Darwin Efficacy Report: Accelerating Data Science at Scale by Automation

White Papers > Data Science > Darwin Efficacy Report: Accelerating Data Science at Scale by Automation

Darwin™ accelerates data science at scale by automating the building and deployment of models. It provides a productive environment that empowers data scientist with a broad spectrum of experience to quickly prototype use cases and develop, tune, and implement machine learning applications in less time.

Darwin’s automated model building capabilities offer unparalleled performance to generate highly accurate models using both supervised and unsupervised learning. Given that this technology is only recently available on an enterprise scale, differentiating between machine learning platforms can be difficult.

Darwin emerged as the system that produced accurate models, particularly for those involving time-series data, datasets with non-linear relationships, or other complex problems. Darwin out-performed competitors in four problem sets.

Darwin displayed comparable results to competitors on the rest of the problem sets. Note that it might take a data scientist days to come up with such a model. Darwin greatly expedites the process of building models by cleansing the data, extracting features, and optimizing models, meaning companies can put these models to use, scale easily, and increase the speed to ROI.

Download the latest white paper from SparkCognition that compares how Darwin performs against other platforms in the market on the same datasets.

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