AI, ML, and Computer Vision Meetup - October 15, 2026

Join our virtual meetup to hear talks from experts on cutting-edge topics across AI, ML, and computer vision. View more Computer Vision events here.

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.

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 deployments based on real project requirements and constraints. Discussion will include what changes at each tier - from development to deployment to monitoring and update strategy - with relevant industry examples.

Attendees will leave with a set of questions or a framework they can use to place their own CV projects into the right tier.

Slice-wise 2D detectors are fast and scalable, but they struggle to produce reliable 3D bounding boxes from volumetric medical data. This talk presents YOLO-PVC, a lightweight post-processing framework that consolidates slice-wise YOLO detections into coherent 3D bounding boxes using percentile-based geometric aggregation and a minimal MLP calibration module.

This talk demonstrates consistent improvements in volumetric IoU across three liver tumor categories i.e., HCC, CCA, and Mixed, without requiring dense 3D annotations or memory-intensive architectures. The talk covers the clinical motivation, the technical approach, and practical lessons from deploying computer vision on real hospital MRI data.