Machine learning best practices in financial services

We recently published a new whitepaper, Machine Learning Best Practices in Financial Services, that outlines security and model governance considerations for financial institutions building machine learning (ML) workflows. The whitepaper discusses common security and compliance considerations and aims to accompany a hands-on demo and workshop that walks you through an end-to-end example. Although the whitepaper focuses on financial services considerations, much of the information around authentication and access management, data and model security, and ML operationalization (MLOps) best practices may be applicable to other regulated industries, such as healthcare. A typical ML workflow, as shown in the following diagram, involves multiple stakeholders. To successfully govern and operationalize this workflow, you should collaborate across multiple teams, including business stakeholders, sysops administrators, data engineers, and software and devops engineers.