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ModelOps Technical Masterclass Series

Learn how to Deploy, Monitor and Govern AI/ML models

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Session #1: Introduction to ModelOp Center

Monday, April 27

In this session, we will examine the various components of ModelOp Center in order to understand the overall architecture of the product, along with what role each of the components play in providing the functionality for putting your models into production. This session will serve as an introduction to build understanding of the various capabilities, so that we can dive deeper on each of the different areas in each of the follow-on sessions.

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Session #2: The Model Catalog

Monday, May 4

In this session, we will examine the data model behind how we store and describe models within ModelOp Center. Models are complex entities that vary greatly between different model factories, and we have a unique way of describing those entities. We will examine the data model itself to understand these mappings on several different kinds of models, and will also examine both the UI representation of the models, as well as the RESTful interfaces for both querying and updating the model catalog.

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Session #3: Runtimes and your Model

Monday, May 11

In this session we will look at how different kinds of runtimes are implemented within the ModelOp product, and what that means within the ModelOp framework. We will first examine the ModelOp Center runtime and how you can enable a model to take advantage of the flexible endpoint binding, and statistics reporting provided. Further, we will examine how runtimes can be run as business endpoints within an enterprise application, or utilized for running ‘batch mode’ operations. We will also examine how to enable models to report both standardized and custom metrics to be captured by ModelOp Center. Finally we will examine how custom runtimes can be integrated with ModelOp Center.

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Session #4: Designing a Model Life Cycle

Monday, May 18

In this session, we will design several different model life cycles utilizing BPMN and the ModelOp Center delegates and signals. We will examine how we can create a model lifecycle to perform periodic back testing within an environment. Further, we will look at how we can create a scaled down business based lifecycle to move a model through the business. Features of this life cycle will include automated testing, approvals through integrated enterprise systems (in this case Jira), and ultimate deployment of the models in a runtime environment. We will also be able to examine some of the lineage generated throughout this process providing the traceability of the model’s journey through the business.

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Session #5: ModelOp Center + BI Tools: measure the business value of enterprise AI initiatives

Wednesday, May 20

In this session we will show how ModelOp Center can interface with Bi Tools to analyze your model’s journey from the Model Factory all the way out to deployment. This will allow us to utilize Tableau and other BI Tools to develop and analyze KPIs for your organization to help speed your models to business. We will also examine other opportunities for integrating BI Tools with ModelOp Center to analyze your models in business.

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