MODEL OPS

Brand Owner Address Description
MINITAB MODEL OPS MINITAB 1829 Pine Hall Road State College PA 16801 MODEL OPS;Software as a Service (SaaS) services featuring software for managing, validating, automating, testing, and maintaining mathematical models, such as machine learning models, in the field of data analysis; software as a service (SaaS) services featuring software with machine learning models for data analytics, data processing, pattern recognition, and for analyzing, compiling, organizing, and monitoring data; software as a service (SaaS) services featuring software for use in centralizing management of machine learning models; software as a service (SaaS) services featuring software for contributing, publishing, discovering, deploying, managing, and governing machine learning models; software as a service (SaaS) services for automating predictive and prescriptive analysis of business data and other data; platform as a service (PaaS) featuring computer software platforms for use in centralizing management of machine learning models; providing temporary use of nondownloadable computer software development tools for use in the field of facilitating the deployment of machine learning models; Providing temporary use of online non-downloadable software, namely, software that fits machine learning models to tabulated quantitative data; cloud computing featuring software with machine learning model functionalities for data analytics, data processing, pattern recognition, and for analyzing, compiling, organizing, and monitoring data; cloud computing featuring software that fits machine learning models to tabulated quantitative data; research and development services, namely, research and development of new products for others in the field of machine learning models;
 

Where the owner name is not linked, that owner no longer owns the brand

    
Technical Examples
  1. Systematic methods to detect, verify, and repair a collinear model are presented. After detecting collinearity in a model or subsets of a model, a directional test is carried out to verify if the collinearity is real. The model can then be adjusted in either direction to making a near collinear model exactly collinear or less collinear, subject to model uncertainty bounds or other linear constraints. When doing the model adjustment, deviations from the original model are minimized and the directionality of the model is kept unchanged.