Reducing Pager Fatigue using a Serverless ML Bot

Mike Fowler, Claranet

Being woken up at 3 am by the pager is never fun but seeing an incident resolve before you’ve even left the bed is maddening. Sleepily the next day you tune the alert for a better night’s sleep yet more untuned alerts sing to you in your sleep. After a few rounds of alert-tuning whack-a-mole you wonder: Could I predict if an incident will resolve itself?

This is the story of how a weary engineer used a Cloud ML model with Cloud Functions to reduce pager noise. Recounting some of the challenges faced, we’ll explore training a model with a limited data set & continual training in a serverless environment. We’ll also explore the implications of using a bot as a first responder to a pager.

Objective of the talk

After this talk listeners will know an architecture for hosting and interacting with a serverless model in Google Cloud. They’ll also appreciate the challenges of working with a limited data set and the implications of automating a task normally handled by human intelligence.

Audience experience required

No experience required although basic familiarity with machine learning terminology will help.

Track 3
Location: Burton and Redgrave Date: October 1, 2019 Time: 4:40 pm - 5:25 pm Mike Fowler, Claranet Mike Fowler, Claranet