AI/ML

Signiant helps M&E companies leverage machine learning today both directly and indirectly, directly by leveraging machine learning in our patent-pending intelligent transport architecture. That architecture looks at a variety of inputs from both current and historical network conditions to determine the best possible path to move a particular data set over a network under the current conditions.

Signiant’s intelligent transport architecture applies machine learning to the vast pool of metadata aggregated across our platform to automatically adapt to changing conditions offering the best performance on any IP network.

Artificial Intelligence and Machine Learning play a larger role in the media industry every day, but many don’t fully understand their mechanisms or benefits. In this webcast, Signiant CTO Ian Hamilton explains that DSP and ML share many common math and technology building blocks.

There are plenty of practical ML and AI applications in use today, and the pace of innovation is moving at a fast clip. Understandably, no business wants to be left behind.

On the surface, it may seem that moving large files from one location to another is relatively simple and that a faster pipe is all you need, but it turns out there’s much more to it. Learn why a bigger pipe doesn’t lead to faster file transfers and how to get the most out of your network.

The fields of Digital Signal Processing and Machine Learning (ML) share many common math and technology building blocks. CTO Ian Hamilton leverages analogy and commonalities between these two disciplines to demystify some of the basic principles underlying how machines learn.