Machine learning is a valuable application for financial services. Organizations gain a competitive edge using ML to mitigate risk, accelerate manual processes and increase customer experiences. But, the data science process can take months to be deployed, and sometimes never even make it to production. The goal of this office hour is to work with AI leaders in the financial services industry to tackle industry specific challenges, to streamline the process from research to production, and to prove ROI of your model to business stakeholders.

Join Solutions Architect, Aaron Schneider in a step-by-step use case for financial service providers. This tutorial will demonstrate how you can use cnvrg.io Flows feature to build custom ML pipelines that run automatically, and deploy the best model to an endpoint of your choice. You will also have an opportunity to ask questions to help you get more models to production.

 

In this session you’ll learn how to:

  

  • Build a custom ML pipeline in Flows
  • Pre-process dataset with Python code
  • Pull XGBoost, SVM and Random Forest from AI Library 
  • Run multiple experiments in parallel with hyperparameter optimization
  • Deploy the champion model as a Web Service on Kubernetes




Machine learning is a valuable application for financial services. Organizations gain a competitive edge using ML to mitigate risk, accelerate manual processes and increase customer experiences. But, the data science process can take months to be deployed, and sometimes never even make it to production. The goal of this office hour is to work with AI leaders in the financial services industry to tackle industry specific challenges, to streamline the process from research to production, and to prove ROI of your model to business stakeholders.

Join Solutions Architect, Aaron Schneider in a step-by-step use case for financial service providers. This tutorial will demonstrate how you can use cnvrg.io Flows feature to build custom ML pipelines that run automatically, and deploy the best model to an endpoint of your choice. You will also have an opportunity to ask questions to help you get more models to production.

In this session you’ll learn how to:

  

  • Build a custom ML pipeline in Flows
  • Pre-process dataset with Python code
  • Pull XGBoost, SVM and Random Forest from AI Library 
  • Run multiple experiments in parallel with hyperparameter optimization
  • Deploy the champion model as a Web Service on Kubernetes
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