I’m a program lead, and I use this site to document my experiments with AI and data—and, hopefully, share what I learn along the way.
Building an autonomous stock trader
Building an agent-based stock-trading system, connecting it to live execution, and examining what an early loss revealed about the design.
Golf Swing Analysis Pipeline — Part 2: LSTM Milestone Detection
Using a bidirectional LSTM and explicit motion features to locate eight ordered golf-swing milestones.
Golf Swing Analysis Pipeline — Part 1: MediaPipe and XGBoost
Turning golf videos into pose landmarks and using an XGBoost gatekeeper to reject unsuitable inputs.
How to Pass the Updated Google Cloud Professional Machine Learning Engineer (PMLE) Exam
What I studied during 30 hours spread over two months, why hands-on practice mattered, and how stale practice questions led me to build Quiz Trail.
Transfer Learning II: You Probably Don't Need to Build Your Own Model
A custom MobileNetV2 took two weekends. Vertex AI AutoML reached the same F1 score for about $5.
Transfer Learning I: Building a CIFAR-10 Classifier
A two-phase MobileNetV2 transfer-learning experiment that reached 91.78% accuracy on a 10,000-image CIFAR-10 evaluation.
Forecasting in SQL: Building an Air Quality Model in BigQuery
Building and validating an air-quality forecasting model in BigQuery ML—and finding the data leakage that would break it in production.
Mapping r/conspiracy via Vertex AI & BigQuery ML
Using Vertex AI and BigQuery ML to classify 1.2 million r/conspiracy posts with a teacher-student pipeline.
Iterating ML models using Weights & Biases
Using Weights & Biases to track experiments and systematically improve a noisy regression model.
Beyond Keywords: Unlock Smarter AI with Semantic Search
Building semantic search with embeddings to retrieve relevant results beyond literal keyword matches.