{"id":33130,"date":"2023-08-14T02:59:00","date_gmt":"2023-08-14T09:59:00","guid":{"rendered":"https:\/\/insidebigdata.com\/?p=33130"},"modified":"2023-08-14T10:40:00","modified_gmt":"2023-08-14T17:40:00","slug":"featurebyte-unveils-its-self-service-feature-platform","status":"publish","type":"post","link":"https:\/\/insidebigdata.com\/2023\/08\/14\/featurebyte-unveils-its-self-service-feature-platform\/","title":{"rendered":"FeatureByte Unveils Its Self-Service Feature Platform"},"content":{"rendered":"
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FeatureByte<\/a>, an AI startup formed by a team of data science experts, announced the release of the FeatureByte self-service feature platform, which radically simplifies and automates the entire feature lifecycle to help enterprises truly scale AI across their organizations. <\/p>\n\n\n\n

One of the key challenges in enterprise AI is the significant time and effort required to prepare, deploy and manage data (also called Feature Engineering). With the FeatureByte platform, enterprises can accelerate AI innovation by enabling data scientists and ML engineers to focus on creative problem solving and iterating rapidly on live data. FeatureByte automatically ensures data consistency in production, yielding better AI data and more performant models. Moreover, the self-service feature platform reduces the need for compute and personnel resources by up to 5X, lowering costs while improving data science productivity.<\/p>\n\n\n\n

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\u201cOne of the biggest problems in enterprise AI right now is the immense number of silos across data, modeling, operations, management, and testing,\u201d said Hyoun Park, CEO and principal analyst at Amalgam Insights. \u201cFeature management is an important strategic initiative for every company expecting to use AI in the long run.\u201d<\/p>\n<\/blockquote>\n\n\n\n

Enterprises can realize a wide range of benefits from the FeatureByte platform, including:<\/p>\n\n\n\n