Monitoring ML Systems - Made With ML

Monitoring ML Systems - Made With ML

Monitoring ML systems to identify and mitigate model performance decay stemming from drift.

8 mentions: @EvidentlyAI@seldon_io@AxSaucedo@EthicalML
Date: 2021/06/16 07:47

Referring Tweets

@EvidentlyAI 📕 A great lesson by @GokuMohandas from @madewithml on monitoring ML systems in production. Thanks for referencing Evidently! t.co/dgyT04OzAw
@seldon_io Learn more about monitoring machine learning systems in the following article on @madewithmlops which features Seldon's own Alibi Detect for drift and outlier detection: t.co/Y4J9opx6n0 #machinelearning #MLOps
@AxSaucedo Monitoring ML Systems Course; A fantastic resource that covers real life and cutting edge techniques for advanced machine learning monitoring. In this hands on course, cover the theory + practical examples on monitoring, drift, outliers, alerting and more t.co/A3FIgnMosm t.co/3zGAo5U63C
@EthicalML Monitoring ML Systems Course; A fantastic resource that covers real life and cutting edge techniques for advanced machine learning monitoring. In this hands on course, cover the theory + practical examples on monitoring, drift, outliers, alerting and more t.co/AQsp0nhk5q t.co/CgujaUZD8u

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