As we enter our 10th year, we wanted to clearly articulate what has shaped the Toronto Machine Learning Summit from the beginning, and why this community continues to matter.
For a decade, TMLS has been shaped by people who build, study, deploy, and live with AI systems in the real world.
That includes practitioners operating production systems, researchers advancing methods, data scientists working inside organizations, and leaders responsible for long-term outcomes.
What connects them isn’t role or title. It’s shared exposure to real constraints: regulation, legacy infrastructure, organizational complexity, and the realities that only surface once systems are in use.
That shared Canadian context matters.
It creates space for conversations that:
- reflect how AI systems actually behave over time
- acknowledge tradeoffs across technical, organizational, and local regulatory boundaries
- value durable lessons over polished “success” stories
TMLS doesn’t revolve around novelty or hype. It’s shaped by a community that takes pride in the systems they work on.
In our 10th year, we’re also welcoming back Research and Executive talk tracks, alongside the practitioner-led core that has defined the event.
Why Lived Experience Matters
Most of the lessons worth learning don’t emerge early.
They show up later, after systems are deployed and pressure tested with scale and load. We’ve seen the same pattern repeat itself from traditional ML to deep learning, to LLMs, and now to agentic systems.
The TMLS community has always reflected that reality. Many people return year after year because the conversations align with how AI work actually works, not just how it’s presented (from both technical perspectives as well as the human systems level).
Our willingness to share these lessons is what allows shared learning to compound across our community.
TMLS 2026 — Dates & Format
📅 June 16–19, 2026
📍 Toronto, Canada
Program structure
- June 16 — Virtual Talks
- June 17–18 — In-person Talks
- June 19 — Hands-on Workshops