Architecting Scalable Machine Learning Infrastructure: Building Robust, Efficient, and Maintainable Systems

Software Architecture

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Architecting Scalable Machine Learning Infrastructure: Building Robust, Efficient, and Maintainable Systems

publish date

Dec 12, 2024

duration

min

Difficulty

Intermediate

Beginner

Case details

In today’s data-driven landscape, the need for scalable and resilient machine learning infrastructure has never been greater. This talk into the principles and practices essential for building robust frameworks that can handle complex data pipelines, model deployment, and continuous integration in a production environment. We will explore the key pillars of scalability, efficiency, and maintainability, discussing strategies to architect infrastructure that supports rapid experimentation and seamless scaling while ensuring high reliability and ease of maintenance. Participants will gain actionable insights into designing systems that support diverse machine learning workflows, accommodate evolving data requirements, and empower teams to develop, deploy, and manage machine learning solutions effectively.

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About Author

Duraikrishna Selvaraju

Software Engineer, Machine Learning

Krishna is a software engineer turned data scientist. After having graduated from the National Institute of Technology, Nagpur, India in 2014, he went on to work in the areas of data engineering, infrastructure engineering, mobile development, and AI technologies across various roles in his career. Krishna previously led the on-premise working group in Kubeflow Open Source ML Platform, representing Cisco. His work there resulted in a talk at the reputed engineering conference Kubecon NA San Diego, 2019. He also worked as a data scientist in the Contact Center AI team and his machine learning work on Agent Assist got awarded as one of the most innovative data science products in Cisco. Before Cisco, Krishna worked in three start-ups in diverse engineering roles. Notably, in SigTuple, a medical AI diagnosis platform, Krishna started early as the eighth employee and went on to lead the AI platform engineering team in the subsequent years. At Meta, Krishna acts as a Senior Machine Learning Engineer to provide technical leadership on combining both the machine learning and engineering fronts for product solutions.