Tired of relying on static threshold-based alerts that don’t seem to provide much value? Do you typically end up finding outliers in your data by staring at lines on your dashboards? We are told machine learning is going make alerts and dashboards smarter, but how? We will help demystify machine learning and provide a practical guide to apply machine learning techniques for numeric outlier detection, and forecasting to make alerts and dashboards smarter and easier to use for actionable results. We will show you the basics of how you can understand your data, get them ready for machine learning, and get the machine to start working for you! You will leave the session beginning to think like a data scientist and knowing how to apply purpose-driven machine learning to your searches in Splunk!

Speaker(s)
Eurus Kim, Staff ML Architect, Splunk
Amir Malekpour, Principal Software Engineer, Machine Learning, Splunk

Slides PDF link - https://conf.splunk.com/files/2019/slides/FN1213.pdf?podcast=1577146259