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 […]
Start integrating our products and tools.
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 […]
Kullback–Leibler divergence is a statistical method for concept drift detection, and is sometimes referred to as relative entropy. The KL divergence tries to quantify how much one probability distribution differs from another, so if we have the distributions Q and P where the Q distribution is the distribution of the old data and P is that of the new data we would like to calculate:
* The “||” represents the divergence.
We can see that if P(x) is high and Q(x) is low, the divergence will be high.
If P(x) is low and Q(x) is high, the divergence will be high as well but not as much.
If P(x) and Q(x) are similar, then the divergence will be lower.
If interested, learn and read more about these concepts in our articles concept drift in machine learning 101 and 8 Concept Drift Detection Methods.
