Neil Stevenson

Principal Architect



Running Machine Learning Analytics On Traces


With over 30 years of industry experience, Neil has designed, developed, debugged, and supported software systems for numerous customers large and small. Initially, a C and assembler programmer, most of the last 20 years have been Java-based, with a focus on distributed systems, data grids, and stream processing. Neil is an occasional committer to the Hazelcast code base, with special interest on GoLang.


Let’s do things differently. To start with, let us view logs and traces as no different from any other data. The data an application indirectly generates when in use (the logs and traces) is no different from the data an application directly works with (input and output). So let’s keep them all together in a scaleable cloud storage repository.

Once it is there, it is just like any other big data. We need to analyze and apply intelligent monitoring to detect situations of interest. So we need to apply trained ML models to a stream of such data for immediate alerting when the traces indicate an unwanted behavior occurring or brewing. This talk will show how to harness existing technologies to do just that.

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