BigML had the pleasure to participate in the inaugural Machine Learning Prague conference, which brought together European companies and startups as well as academics specializing in Machine Learning. To us, it was one more piece of evidence that far from a Silicon Valley fad, Machine Learning is a global phenomenon and the creativity, the talent and the ambition to match those are already at many corners of the world.
In the spirit of passing the knowledge on to thousands more who could not be there, the organizers of Machine Learning Prague have now made video recordings of all the sessions available on their YouTube channel. Among the highlights, you will notice BigML’s Adam Ashenfelter’s presentation on Anomaly Detection. The session starts with a high level review of various anomaly detection techniques and delves into the specifics of the versatile unsupervised Isolation Forest technique, so all in all a great primer into the topic.
Also of note is the presentation by Yandex’s Michael Levin as it explains how Yandex has been able to adopt Machine Learning across their teams by investing into a homegrown platform built mainly on Gradient Boosted Trees. This platform has successfully been applied to many different use cases across the company. As such, it is one more data point in support of a standardized approach instead of relying on custom implementations on a project by project basis. Other examples like the announcement by Facebook, which is prioritizing Machine Learning as a core developer competency is especially striking. Google’s recent article about their ML Ninja program is yet another example. These are great signs that the Wild West era of Machine Learning is coming to a close, and we are seeing a maturing marketplace with tools that can measure up to the biggest unmet challenge: how do we take Machine Learning from being seen as Voodoo Magic to becoming an essential component of every developer’s toolbox?
BigML’s mission has always been democratizing Machine Learning by providing companies of all sizes a consumable, programmable and scalable Machine Learning platform so they can tackle even complex problems with nothing more than their domain expertise, development skills, and the passion to innovate. How so? By providing free educational material, a well-documented API and even a domain specific language to automate sophisticated Machine Learning workflows, implement high level algorithms and share those with others. Let us know your thoughts on how your organization is planning on managing this key transformation.