10x your AI Architect journey with plain‑language, hands‑on guides that blend AI engineering and solution architecture.
- AI Architecture Patterns - RAG, agents, pipelines, and orchestration
- Vector Stores & Embeddings - Storage, retrieval, and similarity search
- Databases for AI - Relational tables, vector indexes, feature stores, and metadata catalogs
- Serving & Scaling - Inference servers, GPUs, and autoscaling
- Inference Infrastructure - GPUs, TPUs, quantization, and cost-performance metrics
- Evaluation & Observability - Testing, monitoring, and cost tracking
- Guardrails - NeMo Guardrails, Rebuff, and custom policy engines
- Observability - Prometheus, OpenTelemetry, drift detection, and alerting practices
- Safety & Security - OWASP LLM Top 10, guardrails, and best practices
- Red Teaming - Scenario generation, attack simulations, and reporting workflows
- Security & Compliance - Regulatory requirements, encryption, and model provenance tracking
- Orchestration Frameworks - LangChain, LangGraph, and workflow tools
- Advanced RAG - Multi-hop retrieval, rerankers, and feedback loops
- Cloud for AI - AWS, Azure, and GCP services and best practices
- Multi-Cloud - Portability, data residency, failover, and cost arbitrage
- Model Routing - Dynamically choose the best model for a task
- Multi-LLM Strategies - Voting, specialists, and cost-aware selection
- Data Scraping - robots.txt, throttling, and content licensing
- Data Lakes & Marts - Lakehouse concepts, ETL/ELT pipelines, and governance
- AI Workflow Automation - DAG schedulers and model CI/CD
- Genetic Memory - Vector memory, knowledge graphs, and retention
- On-Device vs VM - Latency, privacy, and hardware trade-offs
- Courses - AI engineering and solution architecture courses
- Certifications - AI engineering, solution architecture and cloud certifications
- Career Guide - Role snapshots, portfolio tips, and profile building
- Interview Prep - GenAI system design, practice questions, and frameworks
- Solution Architecture Fundamentals - Core architecture principles, patterns, and practices
- Architectural Patterns & Styles - Design patterns and architectural styles for robust solutions
- Architecture Decision Records - Documenting architectural decisions and rationale
- Clean Architecture - Organizing code with dependency inversion and separation of concerns
- Business Architecture - Bridge business strategy and technology implementation
- Technical Architecture - Design robust, scalable, and maintainable technical solutions
- Requirements Analysis - Master requirements analysis and architecturally significant requirements
- Quality Attributes - Master quality attributes and their impact on architecture design
- Architecture Modeling - Master architecture modeling techniques and notations
- Architecture Governance - Establish effective governance frameworks for architectural consistency
- Architecture Assessment & Maturity - Evaluate and improve architectural capabilities
- Architecture Tools & Practices - Master tools, techniques, and practices for effective architecture
- Cloud Infrastructure - Master cloud infrastructure design and deployment
- Estimation Techniques - Master estimation techniques for accurate project planning
- Presales Architecture - Master presales architecture practices for winning proposals
Artificial Intelligence (AI) is reshaping how modern systems are designed. Solution architects are now expected to integrate machine learning models, data pipelines, and intelligent services into scalable and secure architectures that deliver business value.
This repository curates learning resources, certifications, career tips, and interview prep materials to help you grow into an AI‑savvy solution architect or consultant.
- Plain-language explanations of complex AI concepts
- Hands-on checklists and practical implementation steps
- Real-world examples with source attribution and "why it's awesome" explanations
- Visual diagrams using Mermaid for architectural clarity
- Curated resources from official docs, reputable sources, and "awesome" lists
- Career guidance for AI architects, consultants, and solution architects
- Interview preparation with system design frameworks and practice questions
- New to AI Architecture? Start with AI Architecture Patterns
- Choosing between models? Learn Model Routing
- Building RAG systems? Check Vector Stores & Embeddings
- Preparing for interviews? Jump to Interview Prep
- Career planning? Explore Career Guide
Each page follows a consistent structure:
- TL;DR - Simple explanation in plain language
- Quickstart - Actionable steps you can try now
- The Idea - Deeper context and concepts
- Key Concepts - Core ideas with real-world examples
- When to Use This - Clear guidance on applicability
- Real-World Examples - Practical implementations with sources
- Common Pitfalls - What to avoid and how
- Next Steps - Where to learn more
This project is licensed under the MIT License—see the LICENSE file for details.
Made with ❤️ for the AI Architect community
Contributions welcome! Feel free to open issues or submit pull requests.