Build cutting edge integrated data and AI pipelines that transfer from lab to field, without loss of performanceRequest a Demo
Build analytic ready datasets integrating IoT, transactional and geospatial sources and implement industry leading machine learning models that are trained on your integrated datasets
Use a simple drag and drop interface to normalize data, reduce dimensions and build deep learning/machine learning models
Find the best model for your problem by scaling your hyperparameter search to 100s of nodes if desired
Use our patent pending structured learning algorithms to model relationships between predicted outputs even for sparse time-series, geospatial and combinatorial datasets
Maintain a central model registry and manage the ML lifecycle, including experimentation and deployment
In the digital world, data scientists have a linear journey to ROI. For them, building a machine learning model, graduating it to an A/B test environment and pushing it to production when proven is a streamlined process. We, the industrial data science practitioners, have a much more convoluted job. In a three-part article series, we delve on the topic of how to tackle these challenges head on.