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X-WR-CALNAME:Dec 10 - AI\, ML and Computer Vision Meetup
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SUMMARY:Dec 10 - AI\, ML and Computer Vision Meetup
LOCATION:San Francisco\, United States
DESCRIPTION:AI Professionals San Francisco Machine Learning & Agentic AI\nJ
 oin our virtual meetup to hear talks from experts on cutting-edge topics a
 cross AI\, ML\, and computer vision.\n\nDate\, Time and Location\n\nDec 10
 \, 2026\n9:00 AM - 11:00 AM PST\nOnline. Register for the Zoom! (https://v
 oxel51.com/events/ai-ml-and-computer-vision-meetup-december-10-2026)\n\nYo
 ur mAP is fine. Your slices are not. Finding the images that actually matt
 er\n\nA single mAP can look acceptable while one slice is unusable\, small
  objects\, occlusion\, or a confused class. I will run a pretrained detect
 or on a few hundred public images (COCO-style\, not a production crawl)\, 
 report the headline metric\, then break the same run by slice and walk thr
 ough the worst samples.\n\nThe claim is modest: you do not need a million 
 images or a trained-from-scratch model to see what the mean is hiding\, an
 d you cannot relabel everything\, so the next labeling budget should follo
 w those slices. No employer data and no confidential dashboards.\n\nAbout 
 the Speaker\n\nAmandeep Jiddewar (https://www.linkedin.com/in/amanfj/) is 
 a Machine Learning Engineer at Pinterest\, with prior work in U.S. manufac
 turing analytics (CertainTeed / Saint-Gobain) and marketplace allocation (
 QuinStreet). Emory Goizueta MSBA (2019)\, Kaggle Expert. This talk is in h
 is personal capacity and does not represent his current or prior employers
 .\n\nProduction AI Agents: Tool Use\, Evaluation\, Guardrails and Observab
 ility\n\nBuilding an AI agent is easy. Knowing whether it is behaving reli
 ably in production is much harder. This session explores how to evaluate a
 gents that use tools and external systems\, including tool-call validation
 \, tracing\, observability\, failure analysis\, guardrails\, permissions\,
  and human review.\n\nAttendees will learn practical patterns for understa
 nding not just what an agent answered\, but why it acted and whether the a
 ction was safe.\n\nAbout the Speaker\n\nAkshay Talathi (https://www.linked
 in.com/in/akshaytalathi/) is a Vice President at Goldman Sachs with over 1
 0 years of experience building large-scale\, cloud-native\, and distribute
 d systems across financial services and technology. His current focus is G
 enAI and agentic AI\, including production AI agents\, MCP\, enterprise in
 tegrations\, guardrails\, observability\, and reliable AI-driven workflows
 .\n\nFrom Virtual Worlds to Physical AI: Building Simulation Pipelines Tha
 t Survive the Real World\n\nRobots are moving beyond perception systems th
 at only detect and classify objects toward systems that must understand a 
 scene\, learn behaviors\, and act in the physical world. In this talk\, I
 ’ll show a practical Physical AI workflow using OpenUSD\, BowerBot\, NVI
 DIA Isaac Sim and Isaac Lab to build a robot’s virtual world\, including
  its body\, cameras\, sensors\, and environment.\n\nWe’ll explore how de
 monstrations and reinforcement learning can teach behaviors in simulation\
 , how vision and sensor data become observations for a robot policy\, and 
 what needs to match when transferring that behavior to real hardware. The 
 demo follows a small robot from a simulated environment toward a real-worl
 d task\, exposing both the power and the limitations of sim-to-real.\n\nTh
 e goal is to make the path from pixels to actions concrete for computer vi
 sion and machine learning practitioners.\n\nAbout the Speaker\n\nArturo Mo
 rales Rangel (https://www.linkedin.com/in/arturo-morales-rangel) is the fo
 under of Binary Core LLC\, a Physical AI engineering company working acros
 s simulation\, perception\, robot learning\, and deployment.
URL:https://ontown.app/e/twbpk8g3-dec-10-ai-ml-and-computer-vision-meetup/
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