The most advanced ML Observability platform
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 […]
Post-Processing is defined as the processing of a model’s output after the model has been run. This is useful for enforcing fairness constraints without necessarily modifying the model.
For instance, one might post-process a binary classifier by setting a classification threshold to ensure that equal opportunity for a given attribute is maintained by checking whether true positive rates are the same regardless of the attribute in question.
