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Secure ML: Automated Best Practices in Data Science

As data science capabilities scale, the core concept of security becomes growingly critical - in this talk we provide an overview of challenges, solutions and best practices to introduce security into the ML lifecycle.

Abstract

As data science capabilities scale, the core concept of security becomes growingly critical. In this talk we will introduce the security challenges that data science practitioners face across the different phases of the machine learning lifecycle, including experimentation, productionisation and monitoring. We will also cover the set of frameworks and best practices that can be used to mitigate these security challenges at each relevant phase of the machine learning lifecycle. We will use a practical example that will allow data science practitioners to adopt these best practices in their daily workflows to ensure a relevant level of security is present in the multiple stages of the machine learning lifecycle.

Speaker
Alejandro Saucedo
Topic
PyData
Audience Level
Intermediate
Language
English
Duration
30 minutes
Speaker name:
Alejandro Saucedo
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