{"id":22644,"date":"2019-05-30T06:30:55","date_gmt":"2019-05-30T13:30:55","guid":{"rendered":"https:\/\/insidebigdata.com\/?p=22644"},"modified":"2019-05-31T08:38:43","modified_gmt":"2019-05-31T15:38:43","slug":"darwin-machine-learning-platform","status":"publish","type":"post","link":"https:\/\/insidebigdata.com\/2019\/05\/30\/darwin-machine-learning-platform\/","title":{"rendered":"SparkCognition’s Darwin Machine Learning Platform Designed to Accelerate Data Science at Scale"},"content":{"rendered":"\n

As machine learning technology becomes more widely available on an enterprise scale, differentiating and studying which platform can be best for your business can be difficult. <\/p>\n\n\n\n

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Download the full report.<\/a> <\/em><\/figcaption><\/figure><\/div>\n\n\n\n

A new white paper<\/a> from SparkCognition explores one of the solutions on the market that works to accelerate data science at scale. Its Darwin machine learning platform is designed to automate the building and deployment of models. <\/p>\n\n\n\n

According to SparkCognition, the tool works to provide “a productive environment that empowers data scientists with a broad spectrum of experience to quickly prototype and develop, tune and implement machine learning applications in less time.”<\/p>\n\n\n\n

Using its automated model building capabilities, the system works to generate models using both supervised and unsupervised learning. <\/p>\n\n\n\n

In it’s new report, SparkCognition tests the efficacy of its Darwin platform against three open-source products: AutoSklearn, H2O, and Random Forest, while using default parameters with each platform. <\/p>\n\n\n\n

“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.” \u2014 SparkCognition<\/p><\/blockquote>\n\n\n\n

Results were compared on six different datasets comprising each type of supervised learning problem, including: <\/p>\n\n\n\n