About this Event
10th Annual Toronto Machine Learning Summit (TMLS)
TMLS is in it's 10th year, serving as a long-standing community conference bringing together academic research, industry applications, and business strategy in a safe, welcoming, and constructive environment for those working across ML, AI and agents.
Together, our events support a global community of 15,000+ practitioners, researchers, and industry leaders, exploring best practices, methodologies, and principles across three tracks : Research, Business Strategy and Technical Applications.
This year's program structure
This year, TMLS is organized into three core categories, each with focused topic areas designed to reflect how teams actually work, not how conference agendas are usually labeled.
ML/AI Technical / Engineering Talks/Workshops
Hands-on ML and GenAI implementation in real production environments. This is for practitioners building and operating systems, not slideware.
Topic areas include:
- Agent design patterns & architecture
- Agentic workflow automation & orchestration
- AI-assisted development tools and workflows
- Building and extending coding agents
- Data engineering & RAG pipelines
- Enterprise adoption & team design
- Evaluation methods & capability benchmarking
- Fine-tuning & training
- Inference serving & optimization
- LLM prompt engineering & evals
- Monitoring & drift detection
- Safety / governance / auditability
- Search / Recommendation systems
- Tooling extensions & model augmentation techniques
Business / Executive / Product Strategy Talks/Workshops
How organizations decide if, when, and how to deploy AI, and what it takes to make it stick.
Topic areas include:
- AI Adoption & Organizational Change
- AI Strategy & Executive Decision-Making
- Operating Model & Governance
- Product & Go-to-Market Strategy
- Risk, Compliance & Trust
- ROI, Value & Business Impact
- Translating Emerging AI Capability into Deployable Products
Fundamental Research (Capability advancement or novel methods) Talks/Workshops
Novel research and technical exploration that pushes the field forward, without needing an immediate product outcome.
Topic areas include:
- Agentic behavior
- Evaluation frameworks
- Model architecture
- Optimization / search
- Reinforcement learning & control
- Safety / interpretability
- Training methods
What to Expect
- 2 days of hands-on workshops, breakout sessions, and keynotes
- 60+ expert speakers
- 400+ attendees
- Pre-event, app-based networking
- Food, drinks, and social events
- High-quality conversations with people who’ve actually built these systems
This is a working conference. You’ll leave with patterns, tradeoffs, and mental models you can apply immediately, not just inspiration.
Who TMLS Is For
TMLS is designed for data scientists, ML engineers, researchers, product leaders, and executives who are:
- Building or deploying models and agents in production
- Navigating real organizational and regulatory constraints
- Looking to learn from peers, not pitches
Whether you want to deepen your technical judgment, pressure-test strategic decisions, or connect with others who’ve “been there,” TMLS is built to support your growth.
Community First
With a community of 20,000+ ML researchers, practitioners, and leaders, TMLS is rooted in long-term relationships and shared context. Geography matters. Constraints matter. And learning happens faster when people speak honestly with others who operate in the same environment.
We believe real AI progress comes from sharing what actually happened, especially when things were hard.
Accessibility & Values
We believe these conversations should be accessible. Our ticket pricing reflects that commitment.
TMLS is dedicated to advancing the responsible, effective deployment of AI and ML across industries, and helping practitioners fast-track their learning while building meaningful, durable careers in this fast-moving field.
Visit: www.torontomachinelearning.com
Steering Committee & Team
Event Venue & Nearby Stays
CIBC SQUARE, 81 Bay Street, Toronto, Canada
CAD 200.30 to CAD 589.27











