Anyone building enterprise level machine learning pipelines understands how challenging managing dependencies can be, and that's exactly why Conda works its magic. However, these dependencies can come with security vulnerabilities that are becoming increasingly exploited with malware as hackers target popular open source libraries. In this session, we're cover the most common next generation of cyber attacks, like the cryto-mining typo-squatting on Matplotlib, as well as what tools and best practices you can put into place to protect your MLOps pipelines from cybersecurity attacks.
OPEN TALK (AI): Security in MLOps: How to Check for Vulnerabilities in Your Conda Environments
Sal builds data-driven design and deployment processes to reduce cognitive and algorithmic bias in machine learning. Passionate about automating security and site reliability in MLOps, so we can get back to building learning machines to change the word.