Oct 15 - AI, ML, and Computer Vision Meetup

KI-Systeme scheitern oft erst in der produktiven Umgebung. Dieses Online-Meetup analysiert, wie sich Datenqualität, Drift und Modellverhalten nach der Bereitstellung kontrollieren lassen. Jayakumar Ramalingam zeigt anhand von Empfehlungssystemen, wie sich Fehler durch automatisierte Workflows und Monitoring frühzeitig abfangen lassen. Es geht um konkrete Checklisten für den stabilen Betrieb. Ein weiterer Teil widmet sich der Architektur von Computer-Vision-Modellen. Die Entscheidung zwischen Cloud, Edge oder On-Device-Lösungen wird anhand eines praktischen Frameworks beleuchtet.

Join our virtual meetup to hear talks from experts on cutting-edge topics across AI, ML, and computer vision. Time, Place and Location Oct 15, 2026 9:00 AM - 11:00 AM PST Online. Register for the Zoom! Testing AI Systems in Production: Data Quality, Drift, and Model Evaluation AI systems can pass offline evaluation and still fail in production when real-world data changes, features become stale, labels or feedback signals are incomplete, or model behavior drifts away from expected outcomes. This talk shares practical patterns for testing and evaluating AI systems after deployment, including data quality checks, drift detection, online/offline metric comparison, model monitoring, and rollback analysis. Using personalization and recommendation systems as examples, we will examine how teams can build evaluation workflows that catch quality issues before users do. Attendees will leave with a practical checklist for making AI-backed systems easier to evaluate, debug, and operate as data changes over time. About the Speaker Jayakumar Ramalingam is a Staff Software Engineer and Cloud Architect at SiriusXM with over 16 years of experience building cloud-native platforms, real-time data pipelines, resilient APIs, and AI/ML-enabled applications at production scale. Where Should Your Model Live? A Framework for Tiering Computer Vision Deployments Where should a computer vision model actually run - on-device, near the edge, or in the cloud? It's a decision that looks simple until requirements like latency, cost, connectivity, and update cadence start pulling in different directions, often revealing themselves only after deployment. Drawing on hands-on experience developing and deploying CV models across Hailo, Nvidia, Qualcomm and AWS platforms, this talk introduces a practical framework for tiering computer vision deploym...