The CSA MLOps Overview content provides industry guidance on the principles and practices for securely and efficiently operationalizing machine learning models in production. It emphasizes the integration of security practices within the ML lifecycle, aligning with the Six Pillars of DevSecOps to ensure a robust framework for managing machine learning operations. The overview covers the importance of real-world deployment, continuous monitoring, governance, automation, and collaboration across data scientists, ML engineers, and operations teams to maintain model reliability, compliance, and performance in production environments. It aims to help organizations streamline ML workflows with secure, repeatable processes for building, deploying, and monitoring ML models effectively. This guidance supports the adoption of MLOps as a critical discipline to manage the complexity and risks associated with ML systems in enterprise settings.
Publication's URL
https://cloudsecurityalliance.org/artifacts/machine-learning-ops-overviewAdditional documents on this topic
- CIS Secure by Design: A Guide to Assessing Software Security Practices ★★★★★
- OWASP Application Security Verification Standard (ASVS) 5.0 ★★★★★
- NIST SP 800-218 Secure Software Development Framework (SSDF)
- OWASP Top Ten ★★★★★
- MITRE CAPEC – Common Attack Pattern Enumeration and Classification Version 3.9