Single Node Machine Learning
Single Node Machine Learning refers to the practice of training and deploying machine learning models on a single computer or server, without distributed computing across multiple machines. It involves using local computational resources (CPU, GPU, or TPU) to handle data processing, model training, and inference tasks entirely on one node. This approach is common for prototyping, small to medium datasets, and scenarios where low latency or simplicity is prioritized over scalability.
Developers should learn and use Single Node Machine Learning when working with datasets that fit in memory, during initial model development and experimentation, or for production deployments with moderate computational demands. It is ideal for rapid prototyping, educational purposes, and applications where the overhead of distributed systems is unnecessary, such as edge devices, real-time inference services, or small-scale business solutions.