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
Featured Event
Upcoming Events
The Future of Insurance USA 2021
June 22 – 24, 2021
Join our session:
Use AI to Drive Innovative Operational Efficiency 3 Impactful Use Cases:
- AI-driven straight through processing reduces costs and increases customer satisfaction
- Optimize the pricing of your portfolio with AI-driven analysis
- Fraud detection is an ongoing area for improvement and AI is helping
Artificial Intelligence in Financial Services: Applications and Benefits of AI in Finance
October 21st, 2021
Join ModelOp Co-founder Stu Bailey for “Getting your approach to innovation right – Open innovation and strategic partnerships” as part of this event examining the state of Artificial Intelligence’s widespread adoption and its potential to radically transform all facets of the financial services industry.
ML Tools, Infra & Open Source:
Demos | Q&A
June 8th – 10th, 2021
Join our Sales Engineer Matt Laster for a hands-on Demo to learn how to operationalize all models across the enterprise with ModelOp Center, the leading ModelOps platform.
Governance and Risk Management of AI and ML models
June 10th, 2021
Webinar Session Includes:
- Best practices for including MRM controls in the model life cycle
- Automating model risk management processes for increased efficiency
- Bringing together all three lines of defense to centralize processes and enable increased coherence with a centralized production model inventory
- Managing differences in quality and controls of different model types
- Overcome the complexity of monitoring AI and ML models across business units
MLOps World: Machine Learning in Production 2021
June 14th – 17th, 2021
Join our CTO Jim Olsen for a hands-on ModelOps Workshop to learn how to operationalize all models across the enterprise.
Workshop agenda includes:
- Model Life Cycle design
- Actionable Model Monitoring
- Model Governance
On-Demand Sessions
Governance and Risk Management of AI and ML Models
Model risk management isn’t new to Risk and Compliance teams, but AI and machine learning models are. AI and ML models require stringent controls and governance over the processes used to operate them and their outcomes. Creating an automated ModelOps process allows firms to grow and scale their AI initiatives while enforcing the governance, business and risk controls that are not only expected, but required.
In this online discussion featuring executives from ModelOp, Regions Bank, Wells Fargo and Goldman Sachs, learn how model risk management teams can enhance model operations processes to ensure regulatory, compliance and risk requirements and controls are enforced and auditable.
ModelOps vs MLOps – What’s the difference and why should you care
In this presentation from the MLOps: Machine Learning in Production – New York conference, Jim Olsen, CTO at ModelOp, discusses and shows how ModelOps, the discipline of managing all types of models and MLOps, managing only machine learning models, are different in model operational requirements.
2021 ModelOps Summit
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.
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.