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We’re super excited to share that Aporia is now the first ML observability offering integration to the Databricks Lakehouse Platform. This partnership means that you can now effortlessly automate your data pipelines, monitor, visualize, and explain your ML models in production. Aporia and Databricks: A Match Made in Data Heaven One key benefit of this […]
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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 […]
The idea of contextual approaches is to assess the difference between the train set and the test set. When the difference is significant that can indicate that there is a drift in the data.
The idea of Tree Features is to train a relatively simple tree on the data and add prediction timestamp as one of the features. As a tree model can be used also for feature importance, we can know how the time affects the data and at which point. Moreover, we can look at the split created by the timestamp and we can see the difference between the concepts before and after the split.
In the image above we can see that the date feature is at the root and that means that this feature has the highest information gain, so that means that on the July 22nd their may have occurred a drift in the data
Read more about these concepts in our articles Concept drift in machine learning 101 and 8 Concept Drift Detection Methods.
