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 Machine Learning Introduction

  1. Compose a Machine Learing workflow
  2. Add a Machine Learning task
  3. Visualize results from the machine learning workflow
Watch the Machine Learning tutorial video

2 Machine Learning with Facebook Visdom graphical Visualization

  1. General presentation of the Workflow and its execution in the Scheduler
  2. Visdom plot examples
  3. Real-time intrusion detection on Apache log files
  4. Anomaly detection on HDFS log files
  5. Handwritten digits classification
  6. How to create your own Visdom workflow
Watch the ML Facebook Visdom tutorial video

3 Machine Learning Documentation