Experience Alesco’s Proprietary Machine Learning Infrastructure

A distributed machine learning infrastructure to
process the vast and concurrent volume of analytics
that machine learning performs.

We utilize a proprietary, custom-built CPU/GPU based distributed machine learning infrastructure running frameworks such as Spark, Storm, and Chapel to process the vast and concurrent volume of analytics that machine learning performs.  The technology was designed by Decision Science experts and has been influenced by top global academicians in the field.

The purpose-built computing infrastructure has been specifically architected to perform machine learning for direct marketing applications.

Machine Learning for Direct Mail Marketing

What is it and how does it work?

Download Your Free Copy!

If you are struggling with disappointing results from your direct mail programs, machine learning could be your ideal solution. Learn how this type of artificial intelligence is transforming our client’s marketing programs.

Our Approach

Our unique approach to machine learning is based on a ground-up build out of the IT infrastructure rather than using off-the-shelf, black box solutions which have limitations in computational and parallel processing speed and, therefore, produce inferior predictive algorithms.

Our approach is a highly dynamic framework which utilizes simultaneous training of multiple algorithms for each project.  Algorithms adapt to new sets of training data, allowing them to get stronger over time as the latest campaign results are analyzed and the algorithms further refined.

Once only the domain of the largest organizations and academic research facilities, the exponential increases in computing power and decreases in cost are allowing us to bring machine learning to market at price points that are comparable to traditional modeling projects.

Ready to Get Started?

Complete the form below to schedule your personalized consultation with one of our experts.

By submitting this form, you agree to Alesco Data's privacy policy