Are your industrial data science models field ready?

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.

Whitepaper - Geospatial Analytics at Scale

In this whitepaper, we first explain the challenges in geospatial analytics. Then, we explain how DeepIQ addresses these challenges leveraging your execution environment for scalability and customizability.

Whitepaper - Master Data

In this whitepaper, we will explain the challenges in Master Data Management. We will dive into the traditional approaches to manage master data, and then show how DataStudio solves these challenges.

Whitepaper - SAP and PI Integration

DataStudio's built-in connectors for industry standard data historians (e.g. Pi System from OSIsoft) enable seamless fetching of time series data, data cleansing, and data preparation for ML model building exercises and real time execution of validated models.