About us
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
5
Agentic Memory on Databricks: Deploy DB Agent with Databricks Lakebase
·OnlineOnlineRegister: becloudready.com/webinar/db-agent
Fully on Databricks: we deploy the open-source DB Agent live, with Lakebase as the database and, via the pgvector extension it ships natively, as the vector store for agentic memory too, one instance doing both jobs, no separate vector service to provision.
The real problem: enterprises run agents against different databases in different security zones for good reason, and giving them shared memory usually means either a data leak or no memory at all.
The fix: a redaction-first pattern, every interaction passes through a second LLM call that produces a sanitized summary (entities by ID and type, never literal values) before it's written to Lakebase, so other agents get context without ever seeing the underlying data.You'll leave knowing:
- How to deploy DB Agent on Databricks with Lakebase as both database and vector store
- Why pgvector on Postgres beats a bolted-on vector service: similarity ranking, TTL filtering, and self-exclusion in one indexed query, no client-side filtering
- What agentic memory actually means, and why most of what's marketed under that name doesn't survive contact with enterprise security boundaries
- The summarize-then-store redaction pattern, and why it has to be a second LLM call, not a filter
- What broke in real testing (a silent embedding bug), and what the fix looked like
24 attendees
AWS Workshop - Build AWS Data Lake
·OnlineOnline60-minute walkthrough of how engineering teams build production data pipelines on AWS in a single day. We cover architecture, live demo, and what your team needs to run this independently.
Full lab + screenshots: https://becloudready.com/workshops
Learn how to build an AWS data lake from scratch, live, using Amazon S3, AWS Glue, and Amazon Athena. This is a free, hands-on data engineering workshop, not a slide deck: you'll get sandbox AWS credentials and build a real, working data lake pipeline yourself, step by step, in 60 minutes.
We'll take raw CSV data, catalog it with an AWS Glue Crawler, query it with Amazon Athena SQL, then build a Glue ETL job that transforms it into partitioned Parquet for faster, cheaper queries. You'll see the exact cost difference between querying CSV and Parquet on the same data, measured live.
What you'll learn:
- How to build an AWS data lake pipeline from S3 to Athena, hands-on
- What an AWS Glue Crawler does and how it catalogs data without moving it
- How to write and run a Glue ETL job (PySpark) to convert CSV to Parquet
- Why Parquet and partitioning dramatically cut Amazon Athena query costs
- - The raw → catalog → transform → catalog → query pattern used in real-world AWS data platforms
Who should attend:
Data engineers, analytics engineers, cloud engineers, and engineering managers learning AWS data engineering, Glue, Athena, or data lake architecture. No prior Glue/Athena experience needed.Hosted by BeCloudReady (Databricks Registered Partner) and TorontoAI (10,000+ member tech community).
67 attendees
Past events
284
