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Toronto Machine Learning Summit
June 13th - 18th

16 Tracks Carefully Crafted by a Committee of AI Leaders

You’re not the only one under pressure to deliver AI results without clear playbooks. Join peers navigating the same leadership challenges—from aligning cross-functional technical teams to managing vendor complexity and measuring ROI. Gain insights from deep technical (100-400 level) sessions focused on what’s working in the real world – and what’s to come!

Advanced RAG

We go beyond toy RAG demos to show what it takes to build production systems. From naive baselines to GrapRAG and Prolog-based logic, this track unpacks real failures, benchmarks, and breakthroughs in using LLMs to decode complex domains like government policy. If you like this, you may want to check out the Inference Scaling and Data Preparation & Processing tracks. See the full agenda

From modular DAG-based systems in private equity to multi-function agentic chatbots in banking, this track showcases how real agentic systems are being built and shipped. We’ll cover hard lessons, evals that actually matter, and what it takes to move beyond toy prompts to reliable, production-grade AI agents. See the full agenda

Forget checkbox ethics. This track explores how teams are making AI governance real—baking trust, security, and accountability into workflows. From CI/CD-integrated risk scans to role-based GenAI frameworks, it’s where compliance meets engineering. A complement to the Exec and Agentic Systems tracks. See the full agenda

From Vimeo’s multilingual dubbing engine to agentic chatbots in banking and NLP workflows at Reuters, this track highlights real GenAI systems driving measurable productivity gains. It’s where toolchains meet output, and teams get faster—without cutting corners. A complement to Traditional ML and Data Prep tracks. See the full agenda
Careers

The lines between ML researcher, engineer, and AI specialist are blurring—tooling, constraints, and responsibilities are shifting fast. This track shows how roles are evolving, from system integration to agentic infra, and helps you navigate where to grow next.

This track digs into what it really takes to structure data for next-gen systems. Includes LLMs for imputation, unified lakes over stitched silos, and pipelines that hold up in production.
From OpenAI’s work on memory and personalization to Google DeepMind’s vision for general agents and 3D world models, this track dives into what’s shaping the next era of AI. Expect bold ideas on reasoning, adaptation, and where foundation models go next.
What does it take to ship GenAI where privacy and compliance aren’t optional? From OMERS’ call summarization to Thomson Reuters’ due diligence automation and National Bank’s 100% complaint coverage, this track features real deployments in finance, media, pharma, and more.
Hardware Platforms
This track dives into how hardware choices impact every phase of GenAI—covering training on AMD Instinct GPUs, quantization with Brevitas, and fine-tuning techniques using Megatron-LM and HF-PEFT. Explore how compute, memory, and architecture shape performance, cost, and deployment readiness.
LLMs are powerful—but expensive to serve. This track explores the cutting edge of efficient inference, with techniques like quantization, speculative decoding, and FlashAttention. Hear from NVIDIA and d-Matrix, plus a DeepSeek case study on how FlashMLA pushes the limits of throughput without sacrificing quality.
You don’t need a massive platform team to ship reliable AI. This track highlights lightweight, production-ready MLOps strategies—like Vector Institute’s IaC-based inferencing pipeline for AWS and GCP—built for speed, clarity, and constrained resources.
Not every AI experiment leads to impact. This track surfaces what didn’t work—like the organizational roadblocks Google engineers faced when top-down AI hype met on-the-ground complexity. Exploring Real lessons from false starts, stalled rollouts, and why some teams never make it past POC.
Traditional ML
Not everything needs a transformer. This track highlights enduring ML techniques applied in modern ways—Scotiabank’s causal forecasting, Meta’s work on popularity bias in recommender systems, and Tutte Institute’s rethink of unsupervised learning in high-dimensional spaces. Still relevant, still evolving.
As enterprises shift from prototypes to real deployments, verticalized AI agents are emerging as key infrastructure. This track explores how organizations are using tailored agents to solve domain-specific problems, integrate with complex systems, and deliver measurable outcomes at scale. See the full agenda
This track explores models that process video, audio, and images—like MoCha for cinematic-quality talking characters, Vamba for long-form video understanding, and diffusion-powered personalization for retail. If your AI needs to see, hear, and speak, this is the frontier. See the full agenda

Forget checkbox ethics. This track explores how teams are making AI governance real—baking trust, security, and accountability into workflows. From CI/CD-integrated risk scans to role-based GenAI frameworks, it’s where compliance meets engineering. A complement to the Exec and Agentic Systems tracks. See the full agenda

16 Committee Curated Tracks

Public Call for Speakers Open May 1st

Topics to Be Covered
  • Bridging ML Infrastructure/Platform Work to Company Product Impact (Committee pain point)
  • Stakeholder Business Alignment for AI Applications (Committee pain point)
  • Stakeholder Trust in Model Results (Committee pain point)
  • Finding an Easy Way to Connect ML Infrastructure/Platform Work to Company Product Impact (Committee pain point)
  • AI/GenAI Strategy and Product Development (Committee area of interest)
  • Supporting Women and Underrepresented Groups and Promoting Diversity in ML Teams (Committee area of interest)
  • Leadership Challenges in the ML World (Committee area of interest)
  • Managing Employee Adoption of AI Tools (Committee area of interest)
  • Increasing Team Efficiencies and Effectiveness Using LLMs Internally (Committee area of interest)
  • Ensuring AI Safety in Production Environments (Committee area of interest)

Public Call for Speakers Open May 1st

Public Call for Speakers Open May 1st

Public Call for Speakers Open May 1st

Public Call for Speakers Open May 1st

Public Call for Speakers Open May 1st

Public Call for Speakers Open May 1st

Public Call for Speakers Open May 1st

Public Call for Speakers Open May 1st

Public Call for Speakers Open May 1st

Public Call for Speakers Open May 1st

Public Call for Speakers Open May 1st

Public Call for Speakers Open May 1st

Public Call for Speakers Open May 1st

Public Call for Speakers Open May 1st

Public Call for Speakers Open May 1st

Thank You to Our 2025 Committee & Track Leads

Jun 4-5 - Both Conference Days
Expo Sessions and Hallway Track!

Access to the World’s Fair Expo gets you access to dedicated sessions from 30+ speakers, (2024 speakers) Microsoft, AWS, Google, MongoDB, Neo4j, Perplexity, and more, as well as the all important Hallway Track (aka the best way to network with 3000+ AI Engineers)!

Whether you are CEO, CTO, VP of AI, or principal engineer of your company’s “AI council”, the job of the “most senior AI person” at large companies is a lonely one — except here. Learn from and make connections with peers who are all entrusted with high-stakes decisions on company strategy, infrastructure, compliance, and org design.
AI has it’s Bitter Lesson, but AI Engineering’s Sour Lesson is that every engineering problem becomes exponentially harder when deployed at scale. Many frankly never make it, despite claiming success externally. We gather the actual success stories from the largest LLM AI transformations in the world.
Jun 4-5 - Both Conference Days
Expo Sessions and Hallway Track!
Access to the World’s Fair Expo gets you access to dedicated sessions from 30+ speakers, (2024 speakers) Microsoft, AWS, Google, MongoDB, Neo4j, Perplexity, and more, as well as the all important Hallway Track (aka the best way to network with 3000+ AI Engineers)!
Whether you are CEO, CTO, VP of AI, or principal engineer of your company’s “AI council”, the job of the “most senior AI person” at large companies is a lonely one — except here. Learn from and make connections with peers who are all entrusted with high-stakes decisions on company strategy, infrastructure, compliance, and org design.
AI has it’s Bitter Lesson, but AI Engineering’s Sour Lesson is that every engineering problem becomes exponentially harder when deployed at scale. Many frankly never make it, despite claiming success externally. We gather the actual success stories from the largest LLM AI transformations in the world.

Sponsors & Partners

Main Sponsor
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Interested in Partnering? Email Faraz at faraz@torontomachinelearning.com

Who Attends

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Data Practitioners
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Researchers/Academics
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Business Leaders
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2023 Event Demographics

Highly Qualified Practitioners
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Currently Working in Industry*
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Attendees Looking for Solutions
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Currently Hiring
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Attendees Actively Job-Searching
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2023 Technical Background

Expert/Researcher
18.5%
Advanced
44.66%
Intermediate
27.37%
Beginner
9.39%

2023 Attendees & Thought Leadership

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Company Sponsors
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Business Leaders: C-Level Executives, Project Managers, and Product Owners will get to explore best practices, methodologies, principles, and practices for achieving ROI.

Engineers, Researchers, Data Practitioners: Will get a better understanding of the challenges, solutions, and ideas being offered via breakouts & workshops on Natural Language Processing, Neural Nets, Reinforcement Learning, Generative Adversarial Networks (GANs), Evolution Strategies, AutoML, and more.

Job Seekers: Will have the opportunity to network virtually and meet over 30+ Top Al Companies.

Ignite what is an Ignite Talk?

Ignite is an innovative and fast-paced style used to deliver a concise presentation.

During an Ignite Talk, presenters discuss their research using 20 image-centric slides which automatically advance every 15 seconds.

The result is a fun and engaging five-minute presentation.

You can see all our speakers and full agenda here

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