Building AI infrastructure — from data pipelines to GPU kernels. I write about data engineering, LLMOps, GPU optimization, and the systems behind reinforcement learning.
Every LLM is built on five foundational pillars: Basics, Systems, Scaling Laws, Data, and Alignment. This post maps out what they are and why mastering them is the path to building real AI systems.
Read Full Article →Learn how to add observability to LLM applications using OpenTelemetry and data engineering practices.
Read Full Article →Compare ChromaDB, Pinecone, Weaviate, and pgvector to find the right vector database for your RAG application.
Read Full Article →Learn how to orchestrate a Retrieval-Augmented Generation pipeline using Apache Airflow and data engineering best practices.
Read Full Article →Learn how to apply data engineering principles to manage LLM prompts with versioning, labels, and rollback capabilities.
Read Full Article →As a data engineer transitioning into AI, I share why this path makes sense, what advantages we have, and my learning roadmap.
Read Full Article →10 minutes a day. 5 days. One working AI agent. Join the waitlist — launching first week of March.
No spam. Unsubscribe anytime.