AI at Scale in Observability and Security

Discover how the exponential growth of enterprise data and the rise of AI and machine learning are transforming data analysis. This presentation explores the immense value of Large Language Models (LLMs) in processing vast, unstructured datasets for observability and security. We'll delve into the key challenges of implementing AI and LLMs at high volume and scale; from managing high token costs and ensuring data privacy to overcoming team inexperience and the infeasibility of large-scale training. Learn about "shifting left" strategies, including pre-index optimizations and post-index data tiering, and how telemetry pipelines can enable continuous data curation and inference. Join us to understand how to optimize your data streams for AI, gain better control, and achieve significant cost efficiencies in modern observability and security.

Ozan Unlu is the CEO and Founder of Edge Delta, an edge observability platform. Previously he served as a Senior Solutions Architect at Sumo Logic; a Software Development Lead and Program Manager at Microsoft; and a Data Engineer at Boeing. Ozan holds a BS in nanotechnology from the University of Washington.