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
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...
Enhanced IP Geolocation Model for Security and Verification
Designed a novel IP geolocation confidence model combining location observation data with network topography analysis to improve the accuracy of geographic...
Feature engineering systems
Processes transform raw data into structured inputs that are better suited for learning and inference.
Location Observation Fraud Outlier Identification
Used Tableau and sampled data to identify impossible or improbable movement patterns in location observation datasets, then designed a system to tag and filter...
Machine Learning and Data Science
Projects involving predictive modeling, ML pipelines, feature engineering, and data-driven decision systems.
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 —...
Machine learning systems
Pipelines train models on data to produce predictive or generative outputs and improve performance through iteration.
Model serving systems
Infrastructure deploys trained models for real-time or batch inference under latency and scaling constraints.