Radha Ulhe
About Radha Ulhe
Radha Ulhe is a Data Scientist with expertise in time series analysis and log clustering, currently working at OpsMx in Hyderabad, India. She focuses on enhancing software deployment strategies through root cause analysis and AI-based verification processes.
Work at OpsMx
Radha Ulhe has been employed at OpsMx as a Data Scientist since 2018. In this role, she focuses on enhancing software deployment strategies by analyzing time series data and classifying log events. Her work involves detecting anomalous behavior in application performance metrics, which is crucial for improving software reliability. She specializes in applying time series analysis and log clustering techniques to identify performance deviations during rolling upgrades. Additionally, she contributes to AI-based continuous verification processes that ensure new software versions are compatible with existing ones.
Education and Expertise
Radha Ulhe holds a Bachelor's degree in Computer Engineering from Pune Institute of Computer Technology, where she studied from 2005 to 2009. She furthered her education at the International School of Engineering (INSOFE), earning a certification in Data Science and Machine Learning from 2016 to 2017. Her educational background equips her with a strong foundation in both computer engineering and advanced data science techniques, including natural language processing and unsupervised learning.
Background
Before joining OpsMx, Radha Ulhe worked at DeeDee Labs as a Machine Learning Engineer for 10 months from 2017 to 2018. During her tenure there, she developed skills in machine learning applications, which she later applied in her current role at OpsMx. Her experience spans several years in the tech industry, focusing on data analysis and software optimization.
Achievements
Radha Ulhe specializes in utilizing deep analysis techniques for root cause analysis in software performance. She employs advanced methods such as time series analysis and log clustering to enhance application performance during upgrades. Her work on AI-driven continuous verification processes provides critical alerts that aid in decision-making, ensuring software quality and reliability.