Implementing SecMLOps at Every Stage of the ML Pipeline

Explore the critical intersection of security and machine learning operations in this 42-minute conference talk from MLOps World. Delve into the challenges and solutions for implementing SecMLOps at every stage of the ML pipeline. Learn how to navigate compliance requirements and audits confidently while building secure, scalable machine learning systems. Gain insights from Ganesh Nagarathnam, Head of Machine Learning Engineering & Analytics at S&P Global, as he shares his extensive experience in FinTech and regulatory compliance. Discover strategies for incorporating security measures throughout the ML lifecycle, from data engineering to model deployment and monitoring. Understand the impact of data privacy laws on ML projects and explore practical approaches to address security concerns at different stages of ML maturity. Walk away with a comprehensive understanding of SecMLOps and its importance in today's rapidly evolving AI landscape.

Intro
Motivation
Al Security Headline News...
Data Privacy Laws
What does it mean for ML?
Current Challenges
Possible Solutions..@ Org Level
Stages of Security in ML Projects
Stage: Beginner
Beginner Outcome
Stage: Advanced
Stage: PRO
ML System Architecture
ML System Components
SecMLOps - Data Engineering
SecMLOps - Model Deployment & Monitoring
Conclusions