Systems Across Domains
Why cross-domain systems understanding matters, and a library of system-domain notes.
What It Is
Machine Learning is A subset of AI focused on systems that learn from data. On this site, it matters because it transfers across technical, operational, and venture work instead of staying trapped in one narrow context.
Learn more: https://en.wikipedia.org/wiki/Machine_learning
AI inference systems
Compute systems are optimized for executing trained models efficiently at scale.
Breaking Down the AI Agent Term
A practical framework for decomposing the term "AI agent" into twelve distinct dimensions so vague conversations about agentic systems can become precise ones.
Machine learning systems
Pipelines train models on data to produce predictive or generative outputs and improve performance through iteration.
Feature engineering systems
Processes transform raw data into structured inputs that are better suited for learning and inference.
Machine Learning Pipeline for Automated Ad Pricing
Before TensorFlow existed, built a continuously-learning SQL-based machine learning system that optimized real-time bid pricing for programmatic advertising —...
Model serving systems
Infrastructure deploys trained models for real-time or batch inference under latency and scaling constraints.
Python
Python is a programming language for automation, data, and backend systems.
Amazon SageMaker
Amazon SageMaker is a platform for building, training, and deploying machine learning models.
Curiosities and Interests
This page collects the questions, patterns, and topics that repeatedly draw my attention.