AI inference systems
Compute systems are optimized for executing trained models efficiently at scale.
What It Is
Artificial Intelligence is the development of systems that perform tasks requiring human-like intelligence, such as reasoning and learning. 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/Artificial_intelligence
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.
Echo and Source
A music project where I use AI-assisted production and two characters—Echo and Source—to encode philosophical and spiritual truths into repeat-listenable songs.
Introducing Echo and Source
A music project where I use two characters and AI-assisted production to turn lived truths into repeat-listenable songs.
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.
Amazon SageMaker
Amazon SageMaker is a platform for building, training, and deploying machine learning models.
Demographic Prediction Model and Machine Learning Pipeline
Built predictive audience demographic datasets using graph databases and AI/ML. Trained models to predict demographic attributes of mobile device audiences...