Description The complexities of securing MLOps, addressing the unique challenges of managing attack surfaces in this rapidly evolving field. Event Type Virtual, Webinar Event Organizers RiskProfiler Start Date May 23, 2024 End Date May 23, 2024 Registration URL Click here to register
In this event, we dive deep into the complexities of securing machine learning operations, addressing the unique challenges that come with managing attack surfaces in this rapidly evolving field. Here’s what you can expect from the discussion:
Topics of Discussion: 1. Understanding Attack Surfaces in ML Ops: We will explore the various components
that constitute attack surfaces in machine learning environments, including data pipelines, model training, and deployment infrastructure.
2. Best Practices for Securing ML Systems: Learn from Trupti Shiralkar's extensive experience in implementing security programs and standards that safeguard data across the software development lifecycle.
3. Innovative Security Solutions: Discover cutting-edge security solutions that can be integrated into ML Ops to prevent data breaches and ensure the integrity of machine learning models.
4. Real-World Applications and Challenges: Engage in discussions on real-world scenarios where ML systems face security threats, and how to effectively counter these challenges.
