AI / ML · Regulated banking
IRIS MLOps Platform
Turning strategic intent into a production-ready enterprise capability for end-to-end ML model lifecycle management.
Context
From platform ambition to governed delivery.
The initiative required more than technology coordination. It needed a delivery system able to connect data, engineering, architecture, DevOps and business stakeholders around one executable path while the team scaled from 2 to 30+ specialists.
The mandate covered delivery from framing through production readiness, with scope, budget, schedule, backlog, dependencies and executive communication kept under active control.
Delivery system
One operating rhythm across specialist teams.
Cross-functional team
Built and aligned the delivery team across data, engineering, architecture, DevOps and business roles, scaling it from 2 to 30+ specialists.
Dependency governance
Made technical and organizational dependencies visible, with ownership and escalation paths connected to the critical delivery sequence.
Executive visibility
Maintained a consistent view of scope, schedule, budget, backlog and risk so that decisions could be made without losing delivery momentum.
Verified outcome
Global CIO Project of the Year 2021
An enterprise ML lifecycle capability delivered.
The platform enabled end-to-end ML model lifecycle management and was recognized as Global CIO Project of the Year 2021.
Leadership lens
What this case demonstrates.
Complex platform delivery becomes manageable when business intent, engineering work and governance operate as one system rather than as separate reporting streams.