Notes from an AI systems engineer. Tutorials, deep dives, and honest reflections on progressing from data pipelines to GPU kernels.
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 →Test-time training lets models update their own weights during inference. Learn how TTT layers work, their GPU implications, and why this changes AI infrastructure.
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 →Most ML models find patterns in data but can't answer 'why'. I explain why understanding causality matters for building intelligent systems, especially in robotics and reinforcement learning.
Read Full Article →RAG isn't magic - it's Extract, Transform, Load with vectors. I break down how your existing pipeline skills map directly to building production AI systems.
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.
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