Data engineering, stream processing, machine learning and MLOps projects that run on a laptop from a fresh clone, each explaining the system it builds.

Learning MLOps hands-on: the three projects from Jim Dowling's feature store book, rebuilt with open source tools that run locally.

Stopping a language model from inventing SQL over a lakehouse: a semantic layer between the model and Iceberg tables, built with Strands, WrenAI and Trino.

One simulation that writes Parquet for model training and streams Kafka events for live inference, so training and serving use the same simulation code.

An Apache Flink and Kotlin streaming architecture where online machine learning detects concept drift from machinery wear, controlled by a shadow mode router.

Apache Flink, Kafka and Valkey turn a contextual bandit recommender into an event driven service with stateful training and low latency inference.

A Python prototype of a contextual multi-armed bandit recommender that simulates user behaviour and validates the algorithm against cold-start users.