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Predictive analytics traditionally refers to the process of identifying meaningful patterns in historical data in order to predict future trends and events. Having the ability to forecast potential scenarios can help drive strategic decisions.
As every data scientist knows, it takes a significant number of manual steps to go from a business problem with raw data to a fully operational production model.
Most recommendations, promotions, and advertisements people encounter on a daily basis are the result of many complex data pipelines that transform consumer behaviors into targeted predictions.
Having a fully automated detection system at scale is critical for organizations to ensure trust with their customers, however this is incredibly difficult to do effectively in practice. In this blog post, we’ll dive into the mechanics of these systems and talk about some of the traditional approaches.
Today, when enterprises say they are “data-driven,” they primarily rely on a backward-facing approach for making decisions.
Independent audit verifies Kumo’s internal controls and processes. Kumo is proud to announce that we are now SOC 2 Type 1 certified and compliant and SOC 2 Type 2 is in progress.
I often get asked by aspiring founders to share lessons I’ve learned in the one year since the starting of Kumo. At Kumo I wear two hats - one as a co-founder and the second as a head of engineering
In this post, we’ll paint a picture of how effective graphs are in representing many real world problems, most likely including many of the business problems you face.
Kumo, a startup offering an AI-powered platform to tackle predictive problems in business, today announced that it raised $18 million in a Series B round led by Sequoia, with participat...
Founded by former executives at Airbnb, Pinterest, LinkedIn, and Stanford University, Kumo recently launched their next-generation approach to AI over cloud based data...
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