The most advanced ML Observability product in the market
Building an ML platform is nothing like putting together Ikea furniture; obviously, Ikea is way more difficult. However, they both, similarly, include many different parts that help create value when put together. As every organization sets out on a unique path to building its own machine learning platform, taking on the project of building a […]
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We’re excited 😁 to share that Forbes has named Aporia a Next Billion-Dollar Company. This recognition comes on the heels of our recent $25 million Series A funding and is a huge testament that Aporia’s mission and the need for trust in AI are more relevant than ever. We are very proud to be listed […]
Let’s say for a given problem we have a big stable model that uses a lot of data to train – let’s mark it as model A. We will also devise another model, a more lightweight model that trained on smaller and more recent data – it can have the same type. We’ll call it model B.
The idea: Find the time windows where model B outperforms model A. As model A is stable and encapsulates more data than model B, we would expect it to outperform it. However, if model B outperforms model A that might suggest that a concept drift has occurred.
If interested, learn and read more about these concepts in our articles concept drift in machine learning 101 and 8 Concept Drift Detection Methods.