Machine Learning Security

As more and more systems leverage ML models in their decision-making processes, it will become increasingly important to consider how malicious actors might exploit these models, and how to design defenses against those attacks. The purpose of this post is to share some of my recent learnings on this topic. The explosion of available data, processing power, and innovation in the ML space have resulted in ML ubiquity. It's actually quiet easy to build these models given the proliferation of open source frameworks and data (this tutorial takes someone from zero ML/programming knowledge to 6 ML models in about 5-10 minutes). Further, the ongoing trend from cloud providers to offer ML as a service is enabling customers to build solutions without needing to ever write code or understand how it works under the hood.