Experiments, implementation notes, and occasional musings. The common thread is simple: build something, measure it, and write down what changed my mind.
Building and validating an air-quality forecasting model in BigQuery ML—and finding the data leakage that would break it in production.
Using Vertex AI and BigQuery ML to classify 1.2 million r/conspiracy posts with a teacher-student pipeline.
Using Weights & Biases to track experiments and systematically improve a noisy regression model.
Building semantic search with embeddings to retrieve relevant results beyond literal keyword matches.
Building a retrieval-augmented chatbot for small-business information and exploring where RAG helps—and where it does not.
Fine-tuning a small language model on Snoop Dogg and Shakespeare to explore data preparation, tokenization, and causal language modeling.
Fine-tuning a pretrained Vision Transformer on a 6,000-image subset of Fashion-MNIST, with a custom PyTorch training loop and a macro F1 score of 0.9318.