AI MLOps and Security

About

Provides a comprehensive framework for managing production machine learning operations and security, covering the entire lifecycle from data ingestion to incident response. It offers execution-focused guidance on building automated, event-driven pipelines for model deployment, real-time monitoring with rapid 18-second drift detection, and robust safety measures for LLM and RAG systems. This skill is essential for developers and engineers looking to move beyond experimental modeling into reliable, scalable, and secure production environments with built-in recovery protocols and auditable governance.

Key Features

• 22 GitHub stars
• Automated data ingestion using dlt for REST APIs, database replication, and incremental loading
• Real-time drift detection systems with automated retraining triggers and rapid restoration
• Production-ready deployment patterns for batch, real-time, and hybrid ML architectures
• Multi-layered ML security defenses covering prompt injection, jailbreaking, and RAG vulnerabilities
• Comprehensive operational runbooks and monitoring templates for Prometheus and Grafana

Use Cases

• Setting up an automated ELT pipeline to move data from external APIs into a data warehouse
• Architecting and deploying a secure LLM or RAG pipeline with production-grade safety filters
• Designing a model monitoring system that automatically triggers retraining upon detecting performance drift