Share your expertise at TMLS 2026
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TMLS is Canada’s flagship summit for applied ML, AI infrastructure, and enterprise adoption. We bring together the researchers, practitioners, and leaders putting AI into practice across Canada. If you have real lessons, practical wins, or important research to share, we’d love to hear from you.
We’re looking for talks grounded in real work, from production systems and implementation challenges to research that helps the community understand what matters now and what comes next.
A Program Shaped by Practitioners, Researchers, and AI Leaders
TMLS is built for people working through the real challenges of AI adoption, from production systems and infrastructure to operating models, governance, and research breakthroughs.
Every year, the program is shaped by a committee that helps surface the sessions, case studies, and ideas most worth your time.
TECHNICAL / ENGINEERING TALKS
Hands-on sessions for teams building, deploying, and improving ML and GenAI systems in practice, across infrastructure, workflows, optimization, and production-ready engineering.
Topic categories:
- Data engineering and RAG pipelines
- Search and recommendation systems
- LLM prompt engineering and evaluation
- Fine-tuning and training
- Safety, governance, and auditability
- Inference serving and optimization
- Agents, workflow automation, and orchestration
- Monitoring and drift detection
- Enterprise adoption and team design
BUSINESS / EXECUTIVE / PRODUCT STRATEGY TALKS
Sessions for leaders making decisions around AI adoption, organizational design, governance, ROI, and the path from experimentation to meaningful business impact.
Topic categories:
- AI strategy and executive decision-making
- AI adoption and organizational change
- ROI, value, and business impact
- Operating models and governance
- Risk, compliance, and trust
- Product and go-to-market strategy
- Scaling AI from pilot to production
FUNDAMENTAL RESEARCH
Research-led sessions exploring new modeling approaches, algorithms, evaluation methods, and theoretical advances that shape where AI is headed next.
Topic categories:
- Model architecture
- Training methods
- Evaluation frameworks
- Reinforcement learning and control
- Agentic behavior
- Safety and interpretability
- Optimization and search