Draggable machine learning workflow component scheduling method

The invention discloses a draggable machine learning workflow component scheduling method, which comprises the following steps of S1, designing a component configuration template corresponding to a component category, and enabling other nodes except a pseudo node Base in the configuration template to be in one-to-one correspondence with tasks required by machine learning modeling; s2, acquiring tasks and a task sequence required by machine learning modeling included in the current machine learning workflow, and transmitting the task sequence and the configuration template as parameters; s3, dynamically loading and configuring template parameters according to the task sequence, and executing the machine learning workflow. According to the draggable machine learning workflow design thought, workflow modularization can be achieved, rapid reuse can be achieved, and creation of machine learning workflow tasks can be achieved through Web front-end page dragging, code layer and command line modes. 本发明公开了一种可拖拽式机器学习工作