Machine Learning (ML)
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.
Comprehensive Predictive Analytics
Easily investigate data using a wide variety of traditional and modern statistical models, from decision trees to regression models to neural networks.
Adapt to Your Business
Apply business treatments to models and move to prescriptive analytics. Strategically solve for complex problems without needing a PhD in statistics.
Extend ML into Your Existing Infrastructure
Easy code generation means you can quickly build machine learning models and scale them across your enterprise.
Combine Programming Languages
Write data-centric applications using the best programming languages for the job, mixing syntax from different languages inside a single program. Our code and no-code tools empower you to create, maintain and run models and programs using Python, R, SQL, and SAS languages. Leverage a single application that mixes all these languages into a single executable program or workflow with seamless transferring of data between SAS7BDAT, Pandas, and R Data Frames.
Organizations that have invested many man-years in developing IP using SAS language use our tools to continue to maintain and run their existing SAS language programs without the need for any other third-party products.
Begin Your Machine Learning Journey Here
Designed for people with different skill sets, our desktop-based predictive analytics and machine learning solutions will help you quickly generate actionable insight from your data. Quickly build out predictive and prescriptive models that can easily explain and quantify insight found in your data.
Our server-based solution moves all the data-mining computations from the desktop to the server, leveraging more powerful CPU and memory resources as well as larger and faster storage. For users, this means even more efficient data analytics without compromising on the depth of analytics. For IT, this means more control over deployment, security, and user management as permissions for application and file access are controlled by the server’s operating system.
Big Data and ML
Our industry-preferred platform can manage and process vast amounts of data, including its ability to work in-memory with extremely large datasets which is why Altair is included in Big Data architectures. We provide a data science productivity tool that integrates with distributed data structures such as Hadoop HDFS, Amazon S3 and other large-scale distributed file systems. Analytics can easily be done on datasets that have thousands of columns and millions of rows.
Altair Partner Alliance Solutions for Machine Learning
Altair complements our machine learning offering with APA partner technology for time series data, Instant Machine Learning - InstantML through its TIM Studio product. TIM Studio is a best-in-class time series data analytics tool that helps users automate the process to create data models for forecasting and anomaly detection that help them make better-informed business decisions.
The APA offers a number of other data analytics partner solutions to augment our current offering, all of which are available through your Altair Units license.
Featured Resources
Guide to Using Altair Data Analytics to Estimate and Visualize Electric Vehicle Adoption
Data drives vital elements of our society, and the ability to capture, interpret, and leverage critical data is one of Altair’s core differentiators. While Altair’s data analytics tools are applied to complex problems involving manufacturing efficiency, product design, process automation, and securities trading, they’re also useful in a variety of more common business intelligence applications, too. <b> <a href="https://www.altair.com/ev-adoption/"> Explore how machine learning drives EV adoption insights - click here. </a></b> An Altair team undertook a project utilizing Altair Knowledge Studio® machine learning (ML) software and Altair Panopticon™ data visualization tools to investigate a newsworthy topic of interest today: the adoption level of electric vehicles, including both BEVs and PHEVs, in the United States at the county level. This guide explains the team’s findings and the process they used to arrive at their conclusions.
Game-Changing Financial Analytics
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.
Machine Learning in Engineering
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.
Analytics for Heavy Equipment
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.
