Prerequisite: None
This session will include a moderated Q&A featuring questions from the live audience.
Data pipelines form the critical backbone of model development and deployment in machine learning and AI. As these technologies continue to advance and permeate the enterprise, the scalability of data pipelines becomes a pressing concern. This session will explore the unique challenges and opportunities inherent in scaling data pipelines specifically for AI/ML modeling and analytics.
Lakshmi Puroshothaman will discuss why the exponential growth in data volume, velocity, and variety presents both a boon and a hurdle for AI/ML practitioners. Drawing on over 20 years of experience, she will show that more data can potentially lead to more accurate and robust models, and how to avoid the strain it can place on your existing data infrastructure. Purushothaman will discuss why scaling pipelines for AI/ML must address not only the sheer volume of data but also its quality, relevance, and timeliness.