Using Machine Learning to Fill Gaps in Large Datasets

To successfully operationalize machine learning (ML) algorithms, data science teams must be able to continuously update their models so they can improve their predictive performance. The champion/challenger approach is a well-documented method for optimizing models and making adjustments that accounts for changes in the nature and quality of data inputs. Altair’s ML products make it easy to incorporate champion challenger processes into data analytics workflows.

Altair’s data analytics platform helps major banks and nonbank financial institutions transform the mortgage servicing process. Altair tools handle every phase of the data lifecycle, eliminate data silos, and provide decision-makers with excellent visibility into risk and profitability. Click here to explore high impact Altair Mortgage Data Solutions.

Altair’s artificial intelligence (AI) and machine learning (ML) software helps materials scientists understand how to best fill gaps in their material databases, even when it’s impossible to test all possible variants. These advanced tools also optimize testing programs, improve efficiency, and reduce the time required to complete materials testing.

Human resources (HR) is tasked with recruiting the best talent and keeping culture at the forefront while staying on top of payroll, performance, and many other responsibilities. What if you could save time getting these tasks done while your human capital is ahead of the curve to meet your organization’s business goals?

Auditors are under significant pressure to keep expenditures down whether they work for an external audit firm or are part of an internal audit team. Achieving cost-effective audits requires organizations to do more with less - while maintaining or increasing audit quality. To succeed auditors not only need the right expertise and process but also the right data analytics tools.

Stop wasting time repeating the same manual data preparation task in spreadsheets. Experience how Altair Monarch is the solution to fast, automated error-free data transformation.

Machine learning technology leveraging historical and real-time data from sensors mounted to production equipment as well as PLCs, SCADA, and other sources can accurately flag potential failures of whole machines and/or critical components before they can cause downtime.

Most manufacturers must handle large numbers of warranty claims related to a variety of products and components. The volume of claims can easily run to millions per year for consumer goods manufacturers. Patterns within claims data may indicate emerging quality or design problems; therefore, identifying and prioritizing the issues requiring high priority responses is critical to improving quality and reducing the financial impact of claims. This solutions flyer how advanced data analytics tools enable teams to cleanse and organize data from any set of repositories and apply machine learning technology to identify emerging quality or design issues.

Identifying unusual behaviors or patterns in machine components using sensor data can prevent small glitches from creating major operational problems.

Predictive maintenance provides a proactive solution to monitor equipment health and predict failures. It enables organizations to avoid machine failures and unplanned downtime, increase equipment and production line productivity, reduce maintenance costs (including hard component replacement costs and associated personnel time), and improve output quality.

Altair Data Analytics enabled government agencies and people of all skill levels to access, generate, and use smart data to make insightful, informed decisions.

Data Analytics is at the core of driving business value through digital transformation. Transparency in model building and explaining model outcomes leads to confident decision making. Learn how Altair Knowledge Studio fits into an organization’s data-driven approach to solve complex business questions.

There is tremendous opportunity for telco providers to grow Annual Revenue Per User through increased loyalty

It's no industry secret that it costs less to retain existing customers than it does to acquire new ones.

This short video demonstrates machine learning applied to a manufacturing workflow for an organic synthesis process. Altair Knowledge Studio - Altair's solution for advanced machine learning and artificial intelligence - is used in combination with Panopticon - a platform for user-driven monitoring of real-time data.

Scorecards are used by credit lenders to determine the creditworthiness of borrowers, and by marketers to better understand consumer interest in product or service offerings. To help make decisions about an applicant, data from 3rd-party providers that includes delinquency scores, failure scores, and payment ratings; demographic attributes; and the history of current account activity is often used to create a model that predicts the probability of a loan default.