EasyML

Why we included this project

EasyML is a good match for teams that want to apply machine learning to real data without hand-coding every pipeline step. You define a learning task as a directed acyclic graph, where each node is an operation such as a classifier, a data pre-processing step, or a feature transformation, and the system handles scheduling and execution for you. The bundled distributed library, built largely on Spark, covers common classical algorithms plus pre/post-processing and evaluation utilities, so many typical workloads can be assembled without writing low-level distributed code. Existing task DAGs and templates can be cloned and shared, which makes it easier to standardize workflows across a team. The cloud service runs a submitted task, and the GUI lets you configure and monitor experiments from one place.