# mlops
標記為「mlops」的 9 篇文章
LLMOps Security Assessment (Assessment)
Test your understanding of MLOps pipeline security, model deployment attacks, API security, monitoring gaps, model registry poisoning, and CI/CD for ML with 10 questions.
AI Infrastructure Security
Overview of security concerns in AI infrastructure, covering model supply chains, API security, deployment architecture, and the unique attack surfaces of ML systems.
Attacking ML CI/CD Pipelines
Advanced techniques for compromising ML continuous integration and deployment pipelines, including pipeline injection, artifact tampering, training job hijacking, and exploiting the unique trust boundaries in automated ML workflows.
MLflow Security Hardening
Securing MLflow deployments against unauthorized access, experiment tampering, and model registry poisoning.
LLMOps Security
Comprehensive overview of security across the LLMOps lifecycle: from data preparation and experiment tracking through model deployment and production monitoring. Attack surfaces, threat models, and defensive strategies for ML operations.
AI 基礎設施安全
AI 基礎設施安全顧慮的概覽，涵蓋模型供應鏈、API 安全、部署架構，以及 ML 系統的獨特攻擊面。
攻擊ing ML CI/CD Pipelines
進階 techniques for compromising ML continuous integration and deployment pipelines, including pipeline injection, artifact tampering, training job hijacking, and exploiting the unique trust boundaries in automated ML workflows.
MLflow 安全 Hardening
Securing MLflow deployments against unauthorized access, experiment tampering, and model registry poisoning.
LLMOps 安全
Comprehensive overview of security across the LLMOps lifecycle: from data preparation and experiment tracking through model deployment and production monitoring. 攻擊 surfaces, threat models, and defensive strategies for ML operations.