Deepa Elumalai

Site Reliability Engineer, PayPal

Deepa Elumalai is a technologist with over 14 years of experience in software development. She is a passionate problem solver and machine learning enthusiast with a heightened desire to explore technology. She's a strong advocate of the "automate everything possible" ideology. It all started with her journey at HCL where she built simulators and automation test frameworks to automate protocol testing for VoIP devices. Her next venture saw her gain strong PayPal domain expertise as a Site Reliability Engineer and with her problem-solving savviness, she spearheaded the team and built Snap, an "Auto Triage Platform" to troubleshoot incidents and alerts. She explored technologies, brought in insights to triage data by applying machine learning techniques. This transformed the platform to pronounce accurate triage results with very quick response time. She loves cooking and enjoys playing cricket and football with her two adorable sons!

 

Talks on Wurreka:

At PayPal, SRE team troubleshoots production alerts (from ~2500 applications and services). There is always an inherent urgency in resolving the alert. At times, we are swamped with alerts, all requiring attention at the same time.

In this talk, we will share how we have started employing Machine Learning from the ground-up to give our platform the necessary power to predict the probable root cause of alerts.

Also, will elaborate how we use the existing troubleshooting results (from traditional programming) in machine learning to help improve the accuracy of the prediction. The design, working and methodology followed in experimental trials to identify the best model. The model that we built is integrated with our platform and pronounces the root cause in real time. The model has been showing promising results and is a game changer for SREs.

This presentation will mainly walk you through the journey of how we have built the machine learning models and employed the same in production.

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