Infrastructure for deploying machine learning to production in large financial institutions: Lessons learned and best practices
Large financial institutions have many data science teams (e.g., those for fraud, credit risk, and marketing), each often using diverse set of tools to build predictive models. There are many challenges involved in productionizing these predictive AI models. Harish Doddi and Jerry Xu share challenges and lessons learned deploying AI models to production in large financial institutions.
| Talk Title | Infrastructure for deploying machine learning to production in large financial institutions: Lessons learned and best practices |
| Speakers | Harish Doddi (Datatron), Jerry Xu (Datatron Technologies) |
| Conference | Strata Data Conference |
| Conf Tag | Make Data Work |
| Location | New York, New York |
| Date | September 11-13, 2018 |
| URL | Talk Page |
| Slides | Talk Slides |
| Video | |
Large financial institutions have many data science teams (e.g., those for fraud, credit risk, and marketing), each often using diverse set of tools to build predictive models. There are many challenges involved in productionizing these predictive AI models. Harish Doddi and Jerry Xu share challenges and lessons learned deploying AI models to production in large financial institutions. Harish and Jerry begin by outlining the enterprise data science lifecycle and how the production model deployment flow works. They outline the challenges they faced and how they solved them. Along the way, they explain why monitoring models and managing models in the production should be mandatory.
