Prioritising security in Machine Learning is crucial. The implementation of automated and efficient security practices can empower Data Scientists and bolster your team's capabilities. However, acquiring expertise in multiple domains and applying these measures to Machine Learning workloads can be challenging for Data Scientists.

Security has long been a fundamental aspect of DevOps, how can it be applied to Machine Learning?
In this presentation, I will examine the security risks within a Data Scientist environment utilising Amazon SageMaker. Furthermore, I will offer a demonstration of how to leverage the "Amazon Service Catalog" to equip data scientists with ready-to-use templates that incorporate essential security features.
By doing so, we can proactively safeguard our solutions and stay ahead of potential vulnerabilities.