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Join us to learn and practice AI, Gen AI, LLMs, Agents, Machine Learning, Deep Learning together with like-minded developers.
Our goal is to congregate with AI enthusiasts from all over SF bay area to learn and practice AI tech, through tech talks, workshops, code labs etc.. we regularly invite tech leads from innovated companies, successful startups to share their practice experiences and practices in the world of AI, Generative AI, LLMs, Agents, Machine Learning, Deep Learning, MLOps, Data, etc...
If you’d like to speak at our meetups, co-host your events, or inquire about partnership opportunities, please feel free to reach out to us.
https://www.aicamp.ai
Eventi futuri
2
Enterprise AI for LTMs and Structured Data
Entrepreneurs First, 501 Folsom Street, San Francisco, CA, USImportant Note: Register on AICamp website is REQUIRED for admission.
Description:
Join us and FUNDAMENTAL for an evening digging into where LLMs fall short and why enterprises are turning to Large Tabular Models (LTMs) instead. Large Language Models have changed how we work with unstructured data, but most enterprise data still lives in structured tables. It’s here that millions of rows and columns quietly run the actual decisions a business makes. Trillions of dollars in value sit inside these datasets, and traditional LLMs just aren't built to reason over complex schemas, numerical relationships, and high-dimensional tabular data the way they reason over text.Fundamental's research and applied AI leads will walk through LTMs: a new class of foundation models built specifically to unlock the predictive power of structured enterprise data. Expect a relaxed, conversational evening. Good talks. Good questions. Plenty of time to actually meet the people in the room.
The session will cover:
- Why even the most sophisticated LLMs stumble on basic tabular prediction tasks, and where our misplaced confidence in their numerical skills comes from
- How "humble" methods like decision trees still hold their own against generative giants on structured data
- How LTMs differ fundamentally from LLMs in architecture, training, and inference
- What it actually takes to deploy LTM-powered systems in production
- How these models are already driving more accurate, scalable predictions in data-heavy industries like oil and gas, finance, and manufacturing
Speakers:
- Marta Garnelo, Fundamental
- Alexandre Gerbeaux, Fundamental
- Remy Thellier, Snowflake
Speakers/Topics:
If you have a keen interest in speaking to our community, we invite you to submit topics for consideration: Submit TopicsSponsors:
We are actively seeking sponsors to support our community. Whether it is by offering venue spaces, providing food/drink, or cash sponsorship. Sponsors will not only speak at the meetups, receive prominent recognition, but also gain exposure to our extensive membership base of 50,000+ AI developers in San Francisco and 500K+ in global.10 partecipanti
AI Deep Dive (Virtual) - Building Self-Improving Agents
Luogo non specificato ancoraImportant: Register on the event website to receive the joining link. (rsvp on meetup will NOT receive anything).
This is virtual event for our AI global community, please double-check your local time. Can't make it live? Register anyway to receive the webinar recording.
Join Snowflake to learn how to build self-improving agents
Tech Talk: Your Agent Should Fix Itself: Building Self-Improving Agents
Speaker: Elliot Botwick, Principal AI/ML Architect, Snowflake
Abstract: In this talk, we will share how coding agents help developers build high quality agents faster.
A key insight from building agents in production is that high quality agents operate with their goals, plans and actions aligned. We introduce the Agent Goal-Plan-Action (Agent GPA) framework to capture this insight, which achieved state of the art benchmarks on TRAIL/GAIA with 95% error coverage and 86% error localization.
The Agent GPA framework assesses the full agent's process:- Was the goal achieved efficiently?
- Did the plan make sense?
- Were the right tools used?
- Did the agent follow through?
Without visibility into these steps, teams risk deploying agents that look reliable but create hidden costs in production. Inaccuracies can waste compute, inflate latency and lead to the wrong business decisions, all of which erode trust at scale.
We will show how to use coding agents to automate the process of measuring and improving your agent's GPA by using optimization skills that take advantage of the GPA evaluation framework. By the end, you’ll be able to use coding agents and the GPA framework to identify common agent failures, improve their agent and make it ready for production.Venue:
Virtual, join from anywhereMore virtual sessions:
7 partecipanti
Eventi passati
560
