model-deployment

Secure model deployment integrates security linters, SAST/DAST tools, and dependency management into CI/CD pipelines to mitigate vulnerabilities across development workflows, serving infrastructure, and monitoring systems while maintaining compliance with security guidelines.

Model versioning establishes governance practices that track model lineage, enable reproducibility, and support rollback across batch, real-time, and streaming inference strategies, ensuring auditability and compliance in production ML systems.

Model serving monitoring establishes baseline metrics and continuous quality checks to detect data drift and performance degradation, triggering incident response workflows to maintain reliability across production inference endpoints.

Deployment patterns span batch processing for offline predictions, real-time serving for synchronous requests, and streaming inference for continuous data flows, each with distinct serving configurations and monitoring requirements.

Model serving strategies share common governance and monitoring foundations but require different deployment configurations—batch systems use scheduled workflows, real-time endpoints use API serving infrastructure, and streaming uses event processing pipelines.