It’s all about connecting the dots. The more you connect data, the more you learn what’s best for your business. We enable businesses to generate insights from different data points and disparate data. It’s efficient and easy to use, for business analysts and data scientists alike, enabling data science modeling at all skill levels without having to code. After all, data science and machine learning don’t have to be complex to be powerful.
Credit risk specialist builds robust SAS language-powered analytics framework. Vestigo uses Altair Analytics Workbench™ to develop and maintain models and programs written in the SAS language. The software’s drag-and-drop workflow lets its teams build new models quickly without needing to write any code. When the team needs to update existing client libraries, they can work with clients regardless of what language the client used to build them originally since Analytics Workbench can handle Python, R, and SQL in addition to the SAS language. The Vestigo team can combine modules built in any of the four languages into their updated models.
When applied to engineering, Machine Learning can be a powerful tool to aid in a range of applications, from faster finite-element (FE) model building to optimizing manufacturing processes and obtaining more accurate results from physics-based simulations. Although incorporating this collection of technology is relatively new in the field of engineering, Altair has made leaps forward in this space to provide users with the tools they need to make a difference.
Serba Dinamik is an engineering company specializing in operations and maintenance (O&M), engineering, procurement, construction and commissioning (EPCC), and IT solutions for energy exploration and production firms. Their team worked with Altair to develop a Smart Predictive Maintenance Data System (SPMDS) utilizing Knowledge Studio and Panopticon. Maintenance crews use Panopticon-powered dashboards built into SPMDS to monitor every sensor mounted on operating turbines in real time. AI models built with Knowledge Studio identify potential failures or issues that require engineering attention, and, based on that understanding, take turbines offline only when necessary.