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
Once you ensure that your AI/ML models are complete and contain all of the necessary information to help minimize their risk and ensure quality, how do I make sure these models will perform as expected? What steps are best practices before we move the model into the production environment? Once ready to be deployed, what processes are put in place to move the model to production? We will explore these topics and more as we examine the final leg of the journey to move the model into business.
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.