Register to join our four-part Masterclass Series focused on the critical elements of managing AI/ML model risk.
Host: Jim Olsen, Chief Technology Officer, ModelOp
Time: 1:00 p.m. CDT
Duration: 30 minutes (includes Q&A)
October 7: ModelOps, MLOps and Managing Model Risk
In this session we will explore the differences between ModelOps and MLOps, and what this means to your ability to manage model risk. As more and more models are deployed in the business environment, of both traditional and AI/ML models, the risk to your business increases. Whether the risk is direct financial impact to your business, or additional scrutiny by governing agencies, we will explore how a robust ModelOp solution can help you reduce that risk and enable your business to be successful.
October 14: Ensuring the Quality of your Traditional and AI/ML Models
Here we will examine the important steps to take before your AI/ML models are implemented, even in a staging environment. Once a model has been developed and believed to be complete, what additional assets and information should be contained on your model? What additional steps should be taken to ensure the quality of your models, and also help reduce risk further down the line.
October 21: 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.
October 28: Model Monitoring and Retirement
Finally, your model is in production and providing value. Now we will explore the steps needed to ensure the model continues to perform as expected. In doing this, we will look at the various kinds of monitoring that can be performed, and what remediation paths can be taken when the model is not performing as expected. We will also examine the proactive steps we can take to make sure models are kept 'fresh' and don't continue to make predictions past their expected shelf life. Through all of these final steps, we will continue to reduce the overall risk of models to your business.
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