MLOps
Practical MLOps Lessons for Real-World AI Systems
Master MLOps challenges with real-world strategies and engineering judgment. Learn from my experiences building production AI systems.
Blog
Thoughts on AI engineering, backend, frontend, and building modern software.
LLMs
Practical Lessons in LLMs: Real-World Tradeoffs and Implementation Pitfalls
Building production systems with large language models requires balancing speed, cost, and reliability. Learn concrete strategies for deployment, optimization, and avoiding common pitfalls.
RAG & Retrieval
Practical Lessons in RAG & Retrieval: Building Reliable AI Systems in Production
Real-world challenges and design decisions in implementing Retrieval-Augmented Generation for production AI systems
Embeddings & Vector Search
Practical Lessons in Embeddings & Vector Search for Production Systems
Building robust AI systems requires more than models—here’s how to design, optimize, and debug embeddings and vector search in real-world scenarios.
Cloud & Infrastructure
Practical Lessons in Cloud & Infrastructure for AI Engineers
Real-world challenges and design patterns for building scalable, cost-efficient, and production-ready AI systems
AI Engineering
Practical AI Engineering Lessons from Building Production Systems
Learn real-world tradeoffs, failure modes, and design decisions from a senior engineer building AI systems at scale.
AI Agents
Building Production-Grade AI Agents: Lessons from the Trenches
Learn real-world strategies for building reliable AI agents in production, including tradeoffs, failure modes, and design patterns from a senior engineer's perspective.