Events and Webinars

Discover the difference ModelOps can make to your AI initiatives

Upcoming Events

November 30 – December 2 | Chicago, IL

Join our co-founder Stu Bailey to discuss:
A Mandate for MRM teams in the Age of AI

Assessing the impact of regulations alongside the increase of AI adoption
The need for better governance
Establishing new checks and processes that can and must be automated for control and scale
Case study: How has a F200 Bank successfully added automation to the Model Risk Process and cut model validation time by 30%

On-Demand Sessions

Building Automated Model Life Cycles

Presented by ModelOp CTO Jim Olsen at the Toronto Machine Learning Summit 2022

Basics of a model life cycle: What makes up a model life cycle and how do you design one
Governance: Developing an automated governance workflow
Monitoring: How to monitor models post-deployment in a flexible manner
Remediation: Creating remediation workflows that track and accelerate time to resolution

Architecting Scalable AI Operations and Avoiding Key Bottlenecks

Presented by ModelOp CTO Jim Olsen

Scaling AI for an enterprise means more than more data, more compute, bigger speeds and feeds.

Enterprise Architects need to accommodate multiple DSML tools, data systems and serving environments. In addition to the migration to cloud or multi-cloud, the dockerization journey, the increasing use of 3rd party AI models and 4th party AI. How do you implement enterprise standards for Operations and Governance, without stifling innovation and adding complexity?

The Surprising Truth about AI Governance

Presented by ModelOp Co-founder Stu Bailey

Usually when we think “governance” we think ”slow”, “bottleneck”, “anti-innovation”. But the right AI Governance approach goes beyond ethical AI, fairness and bias and also provides:

Visibility to the business contribution of AI efforts and investments
Freedom to innovate with the latest tools and techniques
Faster resolution of problems with less finger pointing and wasted efforts

In this webinar, DS, AI, CoE and CDAO teams will learn how to:

Show the ROI of their AI initiatives
Automate the full model life cycle and establish seamless handoffs across teams
Define and implement reusable governance templates
Automatically ensure all AI Governance policies (e.g. ethics and fairness) are enforced

2022 ModelOps Summit

Join your peers, colleagues, industry and thought leaders, as well as experts in business and technology on Thursday, April 28 at 10 am ET (3 pm GMT), for the second annual virtual ModelOps Summit.

Together, we will dive deep into ModelOps best practices and trends for the future, exploring the challenges at the forefront of AI and ML operations, governance, and risk.

Model Risk Industrialization: A Mandate for MRM Teams in the Age of AI

The growth in AI adoption is accompanied with a parallel growth in regulations targeted at minimizing the risk inherent in AI. Many financial institutions have risk management processes and teams; however, the processes are typically manual and require highly trained model validators, which are in short supply. Because of this, Model Risk teams are unable to keep pace with the growing number of new models, in conjunction with the substantial backlog of existing model validations, re-validations, and annual reviews. This presentation provides real life experiences about how to solve this growing pain.

Model Risk: How the Speed of Digitization Changes Risk

Digital disruption is part of your workflow now and adapting your approach to handling model risk in light of the speed this brings is imperative. In this panel discussion, Banking industry insiders share their strategies for managing this, and still maintaining control and quality.

Panelists:

Stu Bailey, Co-founder and Chief Enterprise AI Architect, ModelOp
Agus Sudjianto, EVP, Head of Corporate Model Risk, Wells Fargo
Krish Swamy, Senior Vice President – Artificial Intelligence, Big Data Analytics and BI, Wells Fargo
Harish Sharma, Advisor, TruEra
Julian Horky, Head of Risk Controlling, Berenberg Capital Markets

End-to-End Governance and Scale of AI and Model Driven Initiatives

Enterprises have strict risk, regulatory and compliance policies today. When it comes to AI, those policies are continuing to evolve. In this presentation, ModelOp Co-founder and Chief Enterprise AI Architect, Stu Bailey shares a case study of a large financial institution that is using AI to better add new layers of defense for fraud detection, and how they successfully established an audit ready ModelOps practice that also reduced model operational costs by 50%.

Leveraging AI as a Source of Competitive Advantage

Enterprises are investing in AI with the intent that it creates market differentiation for them through unique service offerings and improved business operations. But ModelOps (Model Operations) of AI models is still a barrier to high-quality and scalable AI for many organizations. In this discussion, the panelists discuss challenges and best practices for ModelOps based on lessons learned from industry leaders.

Panelists:

Dave Trier, VP Product, ModelOp
Agus Sudjianto, EVP, Head of Corporate Model Risk, Wells Fargo
Jacob Kosoff, Head of Model Risk, Regions Bank
Richa Sachdev, Head of Machine Learning Engineering, Vanguard
Siddharth Mehrotra, SVP, Head of Data Science & Analytics Technology, Citi Velocity – Citi

ModelOp for Insurance Companies: Unlocking the Value of Artificial Intelligence

Stu Bailey, Co-Founder and Chief AI Enterprise Architect shares:

Roadblocks in achieving the benefits of Artificial Intelligence adoption for insurance companies
Top use cases to capture the value of AI
Unlocking the value of AI at QBE Insurance

Increase Model Revenue Contribution

During this 30-minute webinar ModelOp VP Product, Dave Trier and Senior Data Scientist, Sami Merhi highlight Actionable Monitoring capabilities that optimize model performance, enforce risk and compliance controls throughout the model life cycle and ultimately increase model revenue contribution.

The Future of Insurance USA 2021: Thrive in a Digitized Insurance Market With AI

Listen to a distinguished panel of Industry Leaders:
Dan Moore, Chief Operating Officer QBE North America
Rachel Alt-Simmons, Head of Enterprise Business Architecture AXA AL
Stu Bailey, Co-Founder and Chief Enterprise AI Architect ModelOp

Discuss:

Utilize AI to reduce costs and increase customer satisfaction, enabling operational efficiency improvements and speed up time to quote in underwriting complex risks
Improve risk analysis and selection with AI-driven analyses to optimize the pricing of your portfolio
Explore the value of AI in optimizing and screening coverage language, providing transparency in the face of pandemic and cyber concerns

Moderated by Bryan Falchuk, Managing Partner Insurance Evolution Partners.

WSTA Executive TechTalks: AI/Machine Learning & Analytics

Sponsored by ModelOp

In this episode, Equilend’s Dharm Kapadia joins Nemertes’ CEO Johna Till Johnson to break down the current landscape and key considerations for FinServ firms, such as:

Leveraging your current internal talent and finding the right new hires to guide your progress
Managing the integration of available tools & technologies including open source
Examining the future possibilities and limitations of AI and Machine Learning

The First Step in Operationalizing AI Models

In this session from the MLOps World: Machine Learning in Production conference, ModelOp CTO Jim Olsen shows you how to design and build a model life cycle, including how to incorporate Industry best practices as well as provides considerations for creating the model life cycle, who should be involved, and the types of issues that must be considered.

What you will Learn:

Model Life Cycle design
Actionable Model Monitoring
Model Governance

MLOps World Demo Days

In this demo, ModelOp Sales Engineer Matt Laster shows you how to operationalize all models across the enterprise with ModelOp Center, the leading ModelOps platform.

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.