From the course: DevOps for Data Scientists
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Securing the data science models in production
From the course: DevOps for Data Scientists
Securing the data science models in production
- [Instructor] Data science models are application code that needs to be protected and secured like any other production application. Information security is a broad area, but we will focus on four key areas for data science, access controls, which limit who can use the models and related code, software development practices that improve security, such as the use of encryption, operations security, which focuses on system administration security, and finally, disaster recovery, which addresses the need for high availability, even in the cases of severe disruption of some services. Access controls are made up of two kinds of security mechanisms, authentication and authorization. Both are used to control access to models and their outputs. Authentication is the process of identifying a person, process, or device. Logging into a server is a common form of authentication. In general, data scientists should not be developing their own authentication system. That's difficult and…
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