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This year is the 10th Toronto Machine Learning Summit (TMLS).
For a decade, TMLS has been shaped by people who have to own ML outcomes in the real world, in Canada, under the constraints we actually operate in: regulation, procurement cycles, legacy environments, and risk.
...Over the last year, we’ve watched the conversation around “AI adoption” change.
Not because models stopped improving. They’re improving quickly.
The shift is happening somewhere else: in the messy interface between probabilistic systems and real organizat...
Most ML/AI content falls into two camps:
Trend summaries that don’t help you ship Tool promotion framed as insightToday, we’re releasing five long-form Field Notes from production ML/AI systems, built directly from conference talks and post-deployment analysis across the TMLS and ...
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....