Elastic Machine Learning Compute Engine
This project was done as a part of CS5412, cloud computing course at Cornell University
To enable users to analyze their data using standard Machine Learning algorithms out of the box. So, instead of a user configuring an Amazon EC2 instance with his application code and running his algorithm, we will provide this solution through a simple and configurable dashboard. The user has to simply select the ML algorithm he wants to run through the dashboard, the data on which he wants to run the algorithm and the parameters (if any). The user does not have to write application code from the scratch.
Construct an Elastic Cloud model which will provide “Compute as a Service” (CaaS) as a web- service. For the purpose of this project, we intend to provide some standard machine learning algorithms as the compute services. The user can upload data to our servers or give the location (AWS S3 url). The users will also be able to pipe the output of one algorithm to another. This “cloud model” built will ensure scalability, availability, reliability, fault-tolerance, elasticity and security ###Features
- A variety of machine learning algorithms to choose from via a Dashboard.
- Custom datasources like AWS S3 or the user can upload his data to our servers.
- User can generate reports and store them. User will be able to export his results as CSV or JSON or even JPEG images of charts.
- The user interface delivered both as a web application and as an Android app.
- The system auto scales based on load .
For more details on the project, look into the support folder