This repository is a comprehensive educational resource focused on production-grade Generative AI systems, specifically designed for interview preparation and professional reference. Rather than being an executable application, it serves as a detailed knowledge base covering 16 critical areas of modern GenAI deployment, from foundational concepts like RAG systems and embedding technologies to advanced topics including multi-agent architectures, cost optimization, and system design. The content is organized into difficulty-levelled sections (Fresh, Intermediate, Advanced, Expert) with each topic featuring expected answers, common pitfalls (Red Flags), and real-world production statistics drawn from MENA enterprise deployments. What distinguishes this resource is its practical orientation - it emphasizes what actually works in production environments rather than theoretical concepts, with specific attention to trade-offs, failure modes, and optimization strategies observed in real implementations. While the repository contains no executable code and thus has no traditional software architecture or reusable components, it excels as structured educational content. The markdown-based organization allows for easy navigation, and the inclusion of SVG diagrams helps visualize complex architectures. The resource demonstrates particular strength in its coverage of production-specific concerns: evaluation methodologies, observability practices, security guardrails, and cost optimization techniques that are often overlooked in more theoretical treatments of GenAI topics.
The Azure Governance Visualizer (AzGovViz) is a PowerShell-based tool designed for collecting, analyzing, and reporting on Azure governance, compliance, and security data. It iterates through Azure management group hierarchies to gather information from Azure Resource Manager, Microsoft Graph, and other Azure services, then generates multi-format reports (HTML, CSV, JSON, Markdown) for insights into policy compliance, RBAC, cost optimization, security posture, and more. The tool features a modular function architecture, parallel processing for performance, and caching mechanisms to minimize API calls. It is designed for use in Azure DevOps, GitHub Actions, or local execution scenarios and includes capabilities for Azure Landing Zones compliance checking and orphaned resource identification. Despite its usefulness, the codebase exhibits some complexity and tight coupling due to reliance on global state and very large functions, which may present maintenance challenges.
This repository is a collection of AWS EC2 Spot Instance best practices and workshop examples. It contains diverse implementations showing how to use Spot Instances across various AWS services including EC2 Auto Scaling, EC2 Fleet, ECS, EKS, Lambda, SageMaker, AWS Batch, and EMR. The examples demonstrate patterns for handling Spot Instance interruptions, cost optimization, fault tolerance, and workload-specific optimizations. While not a single cohesive application, it serves as a practical reference for deploying cost-effective, interruption-resilient workloads on AWS Spot Instances.