Session: Deep dive into Observability for Distributed Systems
This deep dive will explore practical strategies for implementing observability in distributed systems. Learn how end-to-end observability, driven by telemetry data becomes the cornerstone for analyzing, pinpointing, and swiftly resolving issues in real-world distributed services.
Bio
Deepita Pai is a Senior Software Engineer / Tech Lead at Square, working on Cash App's Health and Identity Platform team. Deepita has had the privilege of working on cutting-edge projects that revolve around safeguarding the integrity of the platform using AI and Machine Learning, and ensuring that only trustworthy customers gain access to Cash App while keeping malicious actors at bay.
Prior to Square, Deepita worked at Amazon where she played a pivotal role in making Amazon.com’s Customer Reviews GDPR compliant. She was responsible for supporting the Customer Reviews widget during 6 Prime Day events - where the service saw a max TPS of 240,000 calls per second.
Her tenure at Twitter Inc. as a Data Scientist in 2019 further exemplified her background in Machine Learning and Artificial Intelligence, where she modeled massive amounts of Twitter data and devised a new Machine Learning model that improved the tweets recommended to a user (with an increase of 7% in engagement rate) on their newsfeed.
Deepita's global experience extends to Munich, Germany, where she served as a Machine Learning Engineer at Brainlab, contributing to the diagnosis of brain signals for disease classification using accelerometer sensors attached to the patient’s head. Her research has been pivotal to contributing to the research on differentiating Strokes from Concussions using brain signals.
During her Master’s, Deepita was a ML researcher at Harvard University. At Harvard, Deepita pursued research in the field of Natural Language Processing where she built an application using a novel algorithm for Harvard researchers to identify diseases based on disease symptoms.