Protecting consumers and enterprises involved in online transactions is just one example of how machine learning (ML) influences our daily lives. In fact, the list of use cases is already long, diverse and growing fast. The reason is clear – ML is a game-changing tool that enables organizations to make better decisions faster. What's more, ML is highly effective at balancing conflicting objectives.

Given the breadth and depth of potential use cases, one thing is clear – more and more people will find themselves working in environments where ML plays a critical role. And thanks to the emergence of low-code and no-code software, ML is no longer the exclusive preserve of programmers, data scientists, and people who paid attention in math class. More of us can, and will, be involved in developing and deploying practical ML solutions.

This eGuide will help you understand the key concepts behind ML, some common applications, and how ML becoming more useful to people at all levels of the modern organization.

If you’re a leader who’s thinking about what digital transformation means for your organization, you may be wondering if data science lives up to the hype. You know that advanced analytics, machine learning, and AI projects have promise, but what’s their actual impact on business results?

This commissioned study conducted by Forrester Consulting on behalf of Altair RapidMiner polls leading digital transformation executives to understand the ROI, challenges, and benefits of starting data science programs so you can better understand what your peers are thinking about and investing in to try and gain a competitive edge.

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.

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.