Events and Webinars
Discover the difference ModelOps can make to your AI initiatives
Events and Webinars
Discover the difference ModelOps can make to your AI initiatives
Upcoming Events
2021 ModelOps Summit
Thursday, April 15, 2021
Keynote Presentation: Erick Brethenoux
VP Analyst and AI Research Agenda Lead, Gartner
Panel Discussions featuring executives from Ally Bank, Charles Schwab, New York Life, Regions, KPMG, Cantor Fitzgerald, and more.
2021 ModelOps Summit
Thursday, April 15, 2021
Keynote Presentation: Erick Brethenoux
VP Analyst and AI Research Agenda Lead, Gartner
Panel Discussions featuring executives from Ally Bank, Charles Schwab, New York Life, Regions, KPMG, Cantor Fitzgerald, and more.
17th Edition Model Risk Virtual Conference
GFMI Conference
February 24-25, 2021
Play Video
Keynote: Model Risk Management in the Age of AI
February 24 at 11:40 a.m. ET
Stu Bailey, ModelOp co-founder
Panel: Operationalizing AI with Proper Risk Management and Controls
February 25 at 9:45 a.m. ET
Moderator: David Trier, VP Product at ModelOp
Panelists:
  • Lourenco Miranda, Managing Director, Head of Model Risk Americas, Societe’ Generale
  • Adam Behrman – Head of Model Risk and Chief Model Risk Officer, Investor Bank
On-Demand Sessions
Model Risk Management in the Age of AI
The three areas driving risk are increasing complexity, regulatory risk and business cost. This keynote addresses these risks as well as how ModelOps can keep AI models compliant and operating as designed.
Operational Scale and Governance of Enterprise AI Initiatives
  • ModelOps best practices to deploy, monitor and govern AI/ML models
  • How customers have been able to scale and govern AI with ModelOps
  • Highlights from the “ModelOps Essential” guide
Governing, Integrating and Implementing Model, Data, AI & ML Initiatives
Hear from Executives from BP, Regions and Wells Fargo, former Citi, as they discuss different aspects of model production (regulatory guidelines, explainability and bias, tracking metrics, speed of deployment & refresh), and market solutions available. They also share their views on organizational aspects of model production, between 1st, 2nd, and 3rd lines of defense and the role of technology, corporate risk, and data science.
Bring Enterprise AI Initiatives into Production with ModelOps
  • Leading Analyst view on the ModelOps space
  • Executive practitioners’ sharing their experiences on how to operationalize all models across the enterprise in a world with models of all types, regulations and the need for explainability and visibility.
  • Lessons learned, what has worked, what hasn’t, and how ModelOps is a key capability for enterprise AI to adapt and thrive in the new normal we are in.
Scaling and Governing Your Enterprise AI Initiatives with ModelOps
  • The main challenges of integrating AI/ML models into existing processes
  • How to best adapt governance to include AI/ML
  • Where should ModelOps live?
  • KPIs for Enterprise AI initiatives
Operationalize the AI Model Lifecycle
ModelOps is breaking down barriers to operationalize AI and ML models. Mike Gualtieri, Forrester analyst and Stu Bailey, co-founder of ModelOp, share how ModelOps and MLOps helps organizations operationalize models.
Model Risk Management Programs in the Age of AI
Shrikant Dash, banking executive and MRM expert, and Stu Baily, co-founder of ModelOp, discuss the emerging Model Risk Management (MRM) requirements for AI/ML models.
A Framework for Analytics Operational Risk Management
H.P. Bunaes, founder of AI Powered Banking, and Stu Bailey, co-founder of ModelOp, discuss best practices for risk management for operational models.
Pass Audits Proactively
AI and ML models are pushing risk management teams to re-evaluate their processes and make sure they are satisfying the evolving regulatory guidelines. Stu Bailey, co-founder of ModelOp and Manish Chakrabarti, Banking Risk Management executive, discuss banking industry risk management requirements.
Operationalizing AI with Proper Risk Management and Controls
Learn how you can responsibly implement and operationalize AI/ML models with controls throughout the model’s life cycle and the right organization, tools and processes.