Top 5 Frameworks for Distributed Machine Learning

Distributed machine learning (DML) frameworks enable you to train machine learning models across multiple machines (using CPUs, GPUs, or TPUs), significantly reducing training time while efficiently handling large and complex workloads that wouldn’t fit into memory otherwise. Additionally, these frameworks allow you to process datasets, tune the models, and even serve them using distributed computing resources.

In this article, we will review the five most popular distributed machine learning frameworks that can help us scale the machine learning workflows. Each framework offers different solutions for your specific project needs.