Applying Machine Learning To The Problem Of Bad Data At Anomalo

Summary

All data systems are subject to the "garbage in, garbage out" problem. For machine learning applications bad data can lead to unreliable models and unpredictable results. Anomalo is a product designed to alert on bad data by applying machine learning models to various storage and processing systems. In this episode Jeremy Stanley discusses the various challenges that are involved in building useful and reliable machine learning models with unreliable data and the interesting problems that they are solving in …

announcements anomalo applications building challenges data data quality machine machine learning machine learning applications machine learning models process processing product storage summary systems