Aspiring MLE portfolio: neural networks, model pipelines, local inference, data science, and ML-powered systems.

PySpark ML pipeline for smart traffic congestion prediction with data generation, preprocessing, model training, evaluation, and streaming-style predictions.

Kaggle-Works is your one-stop repo to organize every phase of a Kaggle competition or tabular-data project. It enforces a clear directory layout so you never lose track of files, and makes it easy to hand off or reproduce your work later.

Knowing how to deploy models into production is as important as building them!

Labs for DeepLearning.AI's Machine Learning in Production course (Andrew Ng) — covers ML system design, deployment, data pipelines, and MLOps.