AI-Assisted, Audit-Ready: Scaling ML Platform and Developer Productivity at A Regulated Fintech

Building ML models is not the hard part for most teams anymore. Getting them from a data scientist’s notebook to a governed, monitored, production system and keeping them there without a platform team standing between every developer and every deploy is. It gets harder inside a regulated fintech, where every model decision needs an audit trail, every alert needs a documented escalation path, and every “quick fix” has to survive a compliance review.

This talk walks through how our ML platform team at KOHO, a Canadian fintech, built a developer-first MLOps stack on AWS that treats governance as a byproduct of good engineering rather than a tax on top of it and how we’re now layering AI-assisted workflows (Claude Code skills, agentic guardrails) on top to remove the remaining manual toil from model onboarding, monitoring triage, and incident response.

We’ll cover the architecture, the AI-assisted developer tooling built on top of it, and what changed operationally as the company moved toward a full banking license, where “move fast” and “pass the audit” had to become the same sentence, not a trade-off.

Audience takeaway: a concrete pattern for developer productivity that survives regulatory scrutiny, plus where AI agents genuinely help.