MLOps North: Building (with) Agents

A gathering for people building agents and the practitioners building with them.
About this Event

MLOps North Toronto Building with Agents

Toronto Summit · Presented by TMLS · Nov 5–6, 2026 · RBC WaterPark Place, TorontoThe people building agents, and the people building with them - one room, on the Toronto waterfront. Two days of practitioner-curated talks on what actually ships: the stack underneath agentic systems, and the products, workflows, and agents teams are running in production right now.

Why come

This isn't another "Intro to RAG" track. It's the things people are debugging, scaling, and fixing this quarter - from teams who've done it. Every session is peer-reviewed by working engineers, not a vendor keynote in disguise.

The theme is split into two tracks

Track 1 — Agents in Production

• Agents in Production
• Agent Infrastructure
• Systems & Reliability
• Deploying Agents at Scale
• Observability & Evals
• Scaling Agentic Systems

Track 2 — Agentic Development

• Agentic Development
• The Agentic Engineering Workflow
• Developer Productivity with Agents
• Engineering with AI Agents
• The AI-Native Dev Workflow
• Tooling & IDE Integration
• Team Adoption & Governance

What's inside

• 2 days, in person
• 10+ tracks
• 35+ speakers
• Talks, hands-on sessions, and hallway conversations with the people running this in production

Who it's for

Software engineers, ML and data engineers, solution architects, infra leads, and the technical leaders (Director, VP, C-suite, founder) making the calls on how AI gets built and shipped.

Details

Nov 5–6, 2026 · RBC WaterPark Place, Toronto waterfront.

Agenda

🕑: 10:55 AM - 11:40 AM
Agentic ML - Leveraging Harnesses to Automate Extraction Models
Host: Aryan Dear, Senior ML Engineer

Info: Model, Data and Objective Drift have traditionally ML Engineering problems that have had a ceiling where we could automate. This talk is a case study of how Wisedocs automated large portions of model development across a 1500+ document types that face standard MLOps challenges. The talk will have three parts, a focus on domain knowledge, the ROI of agentic ML development and sample harnesses and skills we use to enable these capabilities.