Aditya Kelvianto Sidharta
Aditya is a Principal Data Scientist in Capital One, building an automated observability platform that identifies application failure within the system. Throughout his career in the industry, he has an extensive experience in applying causal inference techniques to solve different use cases. This includes building a personalized promo allocation system, surge pricing platform, and root cause identification. Additionally, Aditya obtained his M.S in Computer Science from Columbia University with a specialization in Machine Learning. He actively contributes back to the Data Science community by providing mentorship to students interested in entering the field. He is also involved in pro-bono work, providing technical consultancy to non-profit organizations.
Sessions
The ability to quickly identify and resolve breakages among interconnected microservices is critical for any tech organization running production software. Unfortunately, in most organizations, identifying the root cause of a breakage can take engineers hours of manually sifting through logs and dashboards. In this talk, we’ll describe a fast, automated, and holistic approach to root cause analysis via an ensemble of structural causal models. This talk should be relevant to anyone interested in causal modeling, the field of observability, reliability engineering, or anyone wanting to troubleshoot production software issues faster.