Proactive Machine Learning (PML) is an interactive graphical interface that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. It provides a rich set of generic machine learning tasks that can be connected together to build basic and complex machine learning/deep learning workflows for various use cases such as: fraud detection, text analysis, online offer recommendations, prediction of equipment failures, facial expression analysis, etc. These tasks are open source and can be easily customized according to your needs. PML can schedule and orchestrate executions while optimising the use of computational resources. Usage of resources (e.g. CPU, GPU, local, remote nodes) can be quickly monitored.
This tutorial will show you how to:
1 Create a Machine Learning Task
Create a new Machine Learning task to download easier the Breast Cancer dataset:
2 Publish a Machine Learning Task
Once the task is created it is possible to add it to any bucket. For this example, we will add the new task Load_Breast_Cancer_Dataset to the machine-learning bucket.
3 Delete a Machine Learning Task
Once a task is published in the catalog, it is possible to edit it, manage its versions, delete it, etc. In this example, we will delete the new added task Load_Breast_Cancer_Dataset in order to keep the catalog clean for the other users who will follow the same tutorial.