What we’re about
About TorontoAI
TorontoAI is a vibrant, inclusive community of engineers, builders, founders, and curious minds passionate about making AI infrastructure more accessible, human-centered, and scalable.
We host bi-weekly in-person socials, tech meetups, and hands-on webinars to connect people across disciplines — from DevOps to Data Science, from students to senior architects. Whether you're deploying LLMs in production or just exploring what Databricks does, you're welcome here.
🤝 We’re Building More Than a Meetup
In a world dominated by virtual everything, we believe in real, human-to-human connection.
TorontoAI is a space to:
- Share ideas over coffee
- Spark collaborations face-to-face
- Meet people who understand your stack and your journey
- Build your network beyond LinkedIn likes
💬 What We Talk About:
- Scalable AI & LLM infrastructure (Kubernetes, GPUs, vLLM, Ollama, LangChain)
- Databricks, Snowflake, Fivetran, dbt — building the modern data stack
- MLOps, LLMOps, DevOps — the operational glue of AI systems
- Real-world engineering stories, founder spotlights, and tool breakdowns
🌈 Who We Welcome:
- DevOps, SREs & Platform Engineers moving into data/AI
- Data Engineers, Analysts & ML practitioners
- Founders, freelancers, and technologists in transition
- Students and early-career professionals seeking real-world exposure
We’re committed to creating a welcoming, diverse, and equity-focused space where all voices matter — no gatekeeping, no rockstars, just good humans building cool stuff.
📍 Based in Toronto, open to the world
📅 Join an event — and be part of something human, helpful, and hands-on.
Upcoming events
2
•OnlineFine-Tune AI Model on H100/A100 with Hugging Face
Online### 🔧 Webinar: Fine-Tuning Google Gemma with QLoRA on H100 GPU (Linux Setup + Hugging Face)
🚀 What You'll Learn:
- How to set up a Linux VM with an NVIDIA H100 GPU (Denvr AI Cloud) for ML workloads
- Installing and configuring NVIDIA drivers and CUDA correctly (no more version mismatch headaches!)
- Fine-tuning Google Gemma using QLoRA and Hugging Face Transformers
- Using real-world Text-to-SQL datasets to train LLMs efficiently
- How to save, merge, and test your fine-tuned models
👨💻 Live Demo Includes:
- End-to-end GPU environment setup
- Installing PyTorch, TRL, PEFT, and related libraries
- Downloading and preparing Hugging Face datasets
- Launching Jupyter Notebook to train and run inference
- Model merging and deployment tips
🎯 Key Takeaways:
- Master CUDA + PyTorch compatibility for training
- Optimize for low memory training with QLoRA
- Easily transition from fine-tuning to inference-ready models
📍 Who Should Attend:
- ML Engineers, Data Scientists, MLOps Practitioners
- Anyone training open LLMs (Gemma, Mistral, LLaMA, etc.)
- Developers struggling with driver/CUDA issues on GPUs
We will be using Denvr AI Compute for this Webinar
154 attendees
AI Demo Night - Build Healthcare AI Apps
Toronto, ON, CA## AI Demo Night - Healthcare: Detect Fear-Mongering & Build Business RAG Apps
Registration only accepted through LUMA link - https://luma.com/kh8za3i4
Join us for an interactive session where we explore two exciting real-world applications of AI — from understanding media sentiment to building enterprise-ready RAG (Retrieval-Augmented Generation) apps.
Part 1 — Detecting Fear-Mongering in Media
Ever wondered how fear, exaggeration, or bias spreads through online content?
In this live demo, we’ll use Hugging Face models By Falcons.AI to analyze text, YouTube transcripts, and news articles — identifying fear-based language patterns and emotional triggers.
Learn how NLP pipelines can help measure narrative tone, track media bias, and provide actionable insights for journalists, researchers, and marketers.
You’ll see:
- Live Code deployment - https://github.com/torontoai-hub/fear-monger-detector
- https://huggingface.co/Falconsai/fear_mongering_detection
- How to extract and preprocess text from YouTube or media articles
- How to apply pretrained Hugging Face models for sentiment and emotion classification
- Visualizing “fear-mongering scores” using simple dashboards
***
Part 2 — Deploying Healthcare RAG AI Application to Solve Business Problems
Next, we’ll walk through how to deploy a Retrieval-Augmented Generation (RAG) by Moorcheh.AI based AI assistant that can understand your internal data and answer questions with context — ideal for knowledge management, customer support, or analytics use-cases.
What we’ll cover:
- Building a RAG pipeline with vector databases, open-source LLMs, and your documents
- Deploying the application locally or on Cloud
- Showcasing real business scenarios (e.g., Solving Problem for Nutritionist or Doctor )
***
👥 Who Should Attend
Developers, data scientists, entrepreneurs, and anyone curious about how to apply AI beyond chatbots — to make sense of content and drive value from organizational knowledge.
🗓️ Event Format
- Duration: 90 minutes
- Format: Live demos + Q&A
- Prerequisites: Basic Python knowledge (optional
1 attendee
Past events
269
