Drag-and-drop data pipelining: The next disruptor in ML

Recent advances in machine learning (ML) and artificial intelligence (AI) technologies are helping enterprises across industries quickly move from their use cases from the pilot stage to production and operationalization. According to a report by McKinsey & Company, by 2030, businesses that fully absorb AI could double their cash flow, while companies that don’t could see a 20% decline*. As market pressures increase, data leaders must move beyond point solutions and assess their entire data science and ML ecosystem when considering new ways to leverage technology and reduce time to market. While the number of available ML frameworks has exploded, developing models remains a complex task involving data acquisition, pre-processing, feature selection, modelling, testing, tuning, deployment, etc. Data science teams need a unified platform that encompasses the complete ML lifecycle, fosters collaboration, and centralizes all data science projects in a secure repository.