Events and Webinars
Discover the difference ModelOps can make to your AI initiatives
Upcoming Events
October 2021
Every Thursday in October, join Jim Olsen, ModelOp Chief Technology Officer to learn from his insight and expertise.
October 7: ModelOps, MLOps and Managing Model Risk
October 14: Ensuring the Quality of your Models
October 21: Getting Your Models Ready for Production
October 28: Model Monitoring and Retirement
These 30 minute webinars will take place at 1:00 p.m. CDT, with Q&A immediately following.
October 28, 2021
Explore the differences between ModelOps and MLOps, and what this means to your ability to operationalize models across the enterprise and manage model risk.
November 9-10, 2021
Join ModelOp at the BTF 2021 Flagship Conference which brings together business leaders and technologists from the country’s leading regional and national banks to facilitate the adoption of banking technology & innovation.
ModelOp VP Product, Dave Trier, will host the session “Model Risk: How the Speed of Digitization Changes Risk”.
November 15-19, 2021
Join ModelOp at the 5th Annual Toronto Machine Learning Summit. Despite the vast opportunities that lie within our data, there are explicit challenges, both technical & strategic.
The summit will address common hurdles and celebrate the accomplishments from the community of over 9,000 practitioners, academics & strategists, striving to advance your working potential of ML/AI through shared:
- Workshops
- Interactive Conference Presentations
- P2P Networking
- Career Opportunities and Hiring
ModelOp Sales Engineer, Jon Ouimet, will host a workshop session “Managing AI/ML Model Risk.”
January 26-28, 2022
Join ModelOp at the GMFI 19th Edition Model Risk. This GFMI conference will reflect on the latest updates to model risk guidance from the regulators and what this means for everyday model risk practices. Experts will also explore the impact of market anomalies on model risk and how to take your model risk frameworks to the next level to effectively manage model overlays and incorporate automation for updates. Discussions will also be had into how to overcome explainability challenges of AI models via an effective three lines of defense approach as well as tackling data bias within these models. Finally, assessments will be made into how to create a standardized approach for model validation.
September 29, 2021
Join ModelOp for Arena International’s “AI in Insurance” virtual conference, where over 150 senior executives from leading insurers discuss their latest AI products and success stories, how to leverage new tools, and when and where to use them most effectively.
Stu Bailey, ModelOp Co-founder and Chief AI Architect, will present a detailed case study from QBE insurance, where ModelOp Center is used to automatically deploy and manage AI models for claims triage and fraud detection, ultimately expediting STP.
September 16, 2021
Join the experts from ModelOp for a 30-minute overview on the Actionable Monitoring capabilities that optimize model performance, enforce risk and compliance controls throughout the model life cycle and ultimately increase model revenue contribution.
September 16, 2021
We are a proud sponsor of the Wall Street Technology Association’s “Data, APIs and Analytics Maturity” virtual event which will focus on strategies for measuring ROI and reliability for financial firms’ AI, data and analytics investments.
October 21, 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
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
November 9-11, 2021
ModelOps is a proud GOLD sponsor of Domino Labs Rev 3 – the most ambitious Enterprise MLOPs Leadership Summit on the planet.
On-Demand Sessions
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.