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.
ModelOps Essentials for Operationalizing AI
ModelOps best practices to deploy, monitor and govern AI/ML models
Learn about the challenges faced by large organizations in adopting a ModelOps capability.
Master Class Series
Become an Enterprise AI Architect
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
Master Class Series
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.