Machine Learning

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

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

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

Extend ML into Your Existing Infrastructure

Easy code generation means you can quickly build machine learning models and scale them across your enterprise.

New Cloud-Native Solution

Make Data-driven Decisions

Dynamic tools and collaborative environment to solve complex problems, accelerate transformation, and drive business value

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Begin Your Machine Learning Journey Here

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. Begin your analytics journey by visualizing your data to get a quick understanding of its most important features. Quickly build out predictive and prescriptive models that can easily explain and quantify insight found in your data. Apply and share that insight by deploying models natively or exporting them to common BI tools. Data scientists rely on Altair to efficiently build powerful and insightful predictive models to make better business decisions.

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Take Machine Learning to the Next Level

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.

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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.

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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. Explore how machine learning drives EV adoption insights - click here. 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.

eGuide

Applying Machine Learning Augmented Simulation to Heavy Equipment

Simulation-driven design changed heavy equipment product development forever, enabling engineers to reduce design iterations and prototype testing. Increasing scientific computing power expanded the opportunity to apply analysis, making large design studies possible within the timing constraints of a program. Now engineering data science is transforming product development again. Augmented simulation features inside Altair® HyperWorks® are accelerating the design decision process with machine learning (ML). The power of ML-based AI-powered design combined with physics-based simulation-driven design leveraging the latest in high-performance computing is just being realized.

Technical Document

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.

Technical Document

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.

Customer Stories
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