Kaskada data science automation platform aims to speed machine learning models into production - SiliconANGLE

More than a year after announcing plans to automate the feature engineering phase of artificial intelligence projects, Seattle-based startup Kaskada Inc. is bringing its first product to market. Kaskada says it aims to democratize feature engineering, an often laborious process that requires data scientists to select, clean and validate the data to be fed into machine learning training models prior to moving them into production. A model intended to predict housing prices, for example, would be feature engineered with predictor data such as the square footage of properties, number of bedrooms and location. The larger and more complete the training data set, the better the results. The resources required to collect data and move machine learning models into production can be so significant that the capabilities are out of reach of all but the largest companies.