A live demonstration of the defining ModelOps platform to best orchestrate, visualize, monitor and remediate any model across the enterprise, running anywhere.
Model Risk Industrialization
A live demonstration of how Model Risk teams can keep pace with the growing number of new models, in conjunction with the substantial backlog of existing model validations, re-validations, and annual reviews, and the global shortage of model validators. This masterclass will share how to automate some steps in the model validation process.
Executive Visibility for AI
A live demonstration of a 360-view of the state and status of all models across an enterprise. With this visibility, you will be able to answer such critical questions as:
How many AI models are being used for production business decisioning?
What is the ROI of AI models and initiatives?
Are these models adhering to all risk, governance, and compliance policies and thresholds?
In this session Jim will demonstrate:
Automated processes to drive scale without scaling opex costs
How to provide 24×7 operations and control of all models being used for business decisioning
How to leverage existing investments by integrating with existing IT, data science, Risk, and business systems to support 24×7 processes
Managing AI/ML Model Risk
ModelOps, MLOps and Managing Model Risk
ModelOp Chief Technology Officer, Jim Olsen explores the differences between ModelOps and MLOps, and what this means to your ability to manage model risk.
Ensuring the Quality of your Traditional and AI/ML Models
ModelOp Chief Technology Officer, Jim Olsen examines the important steps to take before your AI/ML models are implemented, even in a staging environment.
Getting Your Models Ready for Production
To ensure models perform as expected, learn how to use best practices before models go to production and what processes are necessary to successfully move those models into production.
Technical Conference Sessions
The First Step in Operationalizing AI Models
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.
ModelOps vs MLOps: What’s the difference and why should you care
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.
Understanding the Model Lifecycle
Design Your First Model Life Cycle
Learn how to build a model life cycle with automated monitoring and retraining
Integrate Business KPIs into the Model Life Cycle
Learn how to incorporate business KPIs into model life cycles for more robust operational decisioning based on business metrics
Add the Functionality of Measuring and Monitoring Bias to Your Model Life Cycle
Learn how to create model life cycles that measure and monitor bias for AI/ML models in production
ModelOps and MLOps Technical Deep Dive
Introduction to ModelOp Center
Learn about the various components of ModelOp Center and gain an understanding of the overall architecture.
The Model Catalog
Learn about the data model used for storing and describing models in ModelOp Center. The data model will be examined to understand the mappings on several different kinds of models, along with the UI representation of the models and RESTful interfaces for querying and updating the production model inventory.
Runtimes and Your Model
Learn how different runtimes are implemented in ModelOp Center and what that means within the ModelOp framework.
Designing a Model Life Cycle
In this session we will design several different model lifecycles utilizing BPMN and ModelOp Center delegates and signals.
Model Center + BI Tools: Measure the Business Value of Enterprise AI Initiatives
Learn how ModelOp Center interfaces with BI tools to analyze a model’s journey from the model factory to deployment.
Any reference made to “ModelOps” is about model operations platforms, not the company ModelOp. Gartner does not endorse any vendor, product or service depicted in its research publications and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s Research & Advisory organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.