Machine Learning Operations Made Easy
Today, we are happy to share that BigML Ops is now available to BigML users including both our MLaaS subscribers on BigML.com and our private deployment customers. Let us give you a little bit of a background on how we got here before describing the exceptional features that BigML Ops brings to the market.
Since BigML’s inception back in 2011, BigML models have been automatically and immediately operational upon creation, and the predictions generated with those models have been completely traceable back to the models, evaluations, datasets, and data sources associated for the sake of full transparency and reproducibility. BigML allows you to immediately make predictions from your models as soon as you create them. Models and Predictions are accessible as separate REST resources and can be consumed using many libraries. As a matter of fact, in BigML, each modeling entity is a REST resource and that’s a key design choice setting the platform apart from competitors. It affords a level of flexibility nobody offers when it comes to building and consuming models.
These REST resources can be consumed by a multitude of smart applications you may choose to integrate with. This is literally as friction-free as model operationalization can be. Even though this seemingly is a trivial concept, over the years, we had to explain it many times because most modelers were either used to traditional statistical tools where this level of usability has been pretty much unthinkable or they had learned Machine Learning with open source tools like scikit-learn where real-world operationalization concerns were never part of the original design.
