This video demonstrates the difference between single-machine and distributed machine learning using a practical implementation.
We train a Random Forest Classifier using scikit-learn on a large dataset and compare two approaches:
🔹 Single Machine Training
The model is trained on one computer using all available CPU cores.
🔹 Distributed Training
The dataset is split across multiple machines and processed in parallel using Ray.
