Machine Learning Open Studio (ML-OS) 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 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. ML-OS 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 Customize the Canvas
Manage the Canvas to be able to easily drag and drop machine any task in the learning and data visualisation buckets.
2 Build the Diabetics Detection Workflow
3 Submit and Visualize Results