Srinivasan Ramanujam is a Vice President at Standard Chartered Bank, with over 22 years of experience in the development and support of Enterprise Applications. Certified in TOGAF, Multi-Cloud, DevOps, SRE, and Machine Learning, he possesses a high level of expertise in analyzing, designing, and implementing cost-effective solutions.
As an industry leader, he has spearheaded process improvements, reducing time to market for critical products and surpassing client expectations. Srinivasan has shared his knowledge through insightful whitepapers and presentations on topics like Predicting Service Level Objectives with Machine Learning and Demystifying the Relationship between Probability and Site Reliability Engineering
Srinivasan Ramanujam’s academic pursuits include an Executive Post Graduate Program in Machine Learning & AI from IIIT Bangalore, and holds a Master of Technology(M.Tech) in Energy Technology. He’s earned a myriad of certifications, from Microsoft and IBM to Google and AWS, showcasing his commitment to staying at the forefront of technological advancements. He has held key positions in renowned companies such as Cognizant Technology Solutions, Capgemini, and Accenture, contributing significantly to their success.
Apart from his technical prowess, Srinivasan actively contributes to industry associations like the Association of Enterprise Architects (AEA) and the Project Management Institute (PMI). Passionate about sharing knowledge, he maintains an active online presence on LinkedIn (https://in.linkedin.com/in/ramanujamthearchitect) and GitHub (https://github.com/srinirama). on LinkedIn and GitHub.
Alongside his professional pursuits, he engages in hobbies such as Yoga, cycling, and reading.
UNVEILING MATHEMATICAL RELATIONS IN SITE RELIABILITY ENGINEERING (SRE) & OPTIMIZING SERVICE LEVEL OBJECTIVE (SLO) USING MACHINE LEARNING (ML)
This talk explores the essential mathematical foundations that underpin Site Reliability Engineering (SRE) practices, unveiling the undiscovered Mathematical Relationships in SRE, and delving into the intricacies of maintaining robust and reliable digital services. We will unravel the core mathematical principles employed in SRE, shedding light on their significance and thereby sustaining reliability and stability.
Furthermore, the presentation will showcase the integration of cutting-edge Machine Learning (ML) techniques to enhance Service Level Objective (SLO) optimization within the realm of SRE. By leveraging ML, we aim to streamline and automate the decision-making process, enabling SRE teams to proactively manage and optimize service reliability.
Attendees will gain insights into the symbiotic relationship between mathematics and SRE, witnessing how the fusion of these disciplines, coupled with ML advancements, can revolutionize the approach to maintaining resilient and efficient digital infrastructures. Join us on a journey through the mathematical landscape of SRE, where theory meets practical application, and learn how ML is transforming the landscape of Service Level Objective optimization.