Functions, Users, and Comparative Analysis
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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 […]
Fundamentals of ML observability
Metrics, feature importance and more
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 Time Window Distribution model focuses on the timestamp and the occurrence of the events.
The idea of ADWIN is to start from time window W and dynamically grow the window W when there is no apparent change in the context, and shrink it when a change is detected. The algorithm tries to find two subwindows of W – w_{0} and w_{1} that exhibit distinct averages. This means that the older portion of the window – w_{0} is based on a data distribution different than the actual one, and is therefore dropped.
