Observability at the long tail: why sampling production data doesn’t work for rare events

Machine learning has a long-tail problem: even if our models work well in the average case, they often fail and lack proper monitoring for rare events. This is a concern because there are many business use cases where poor performance at the tail of the distribution can be detrimental to customer satisfaction or other metrics. At WhyLogs, we have done a number of experiments that illustrate the issues with the long-tail problem and how proper monitoring is needed to prevent catastrophic failures we've seen in the past. We built whylogs, an open-source library using data profiling instead of data sampling. This allows for better accuracy and precision while still scaling to petabyte scale data in both batch and streaming modes.

In this talk, we walk through example cases and experiments to demonstrate the failure cases of evaluation and monitoring on sampled data. We also discuss how we’ve addressed these issues using data sketching techniques. Finally, we offer an open source library, whylogs, that enables statistical data logging and profiling in only a few lines of code.