Like all businesses during the pandemic, banks and financial institutions are facing numerous challenges — one of the biggest being the increased difficulty in accurately predicting the evolution of their businesses when new patterns and indicators emerge daily in this volatile global market. The good news is that AI can make a notable difference for these organizations when facing such instability, not so much at the algorithm or model-level, but at the testing and validation level.
In this webinar, DJ Human, Customer-Facing Data Scientist at DataRobot will discuss the potential for MLOps and challenger models to create simulation and A / B testing scenarios for the banking industry, especially in the context of the uncertainty we are experiencing today. The insights gathered from these tests have the potential to greatly improve the decision-making process.
In this webinar, you will learn how to:
- Use MLOps to help financial institutions when dealing with a variety of high-risk areas like credit scoring, insurance pricing, fraud, and other relevant scenarios
- Use MLOps to run traditional A/B tests and compare model performance on decisions made in production
- Use DataRobot’s enterprise AI platform to build high-quality alternative models easily and constantly as potential challengers
- Use DataRobot MLOPs champion / challenger framework to run challenger models in the background, alongside current best-performing champion models
Customer Facing Data Scientist at DataRobot
With MLOps, we were able to deploy both DataRobot and non-DataRobot models within minutes rather than weeks, enabling us to achieve a far faster time to value than with homegrown deployments. In addition, the monitoring capabilities ensure that our models are generalizing appropriately to new data. We have so far had 100% uptime on our deployments.
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