DataScienceKit
A Cloud Platform for Developing, Running and Deploying Data Science Applications
The aim of DataScienceKit is to provide a fully hosted solution for data science in self-hosted, on-premise and cloud environments.
One of the problems that is commonly faced by the data science community is that, while it is relatively easy to open a Jupyter notebook and create a visualization using open source datasets, it is relatively difficult to deploy the visualizations to a production environment and also to find high quality data sets to train new ML models on. This is where DataScienceKit comes in.
DataScienceKit or DSKit provides bindings in various languages and runtimes to visualization frameworks in both self-hosted and cloud-hosted environments for ingesting as well as processing and visualization of big data.
The process of consuming data by data scientists has always been a process which is error-prone and filled with trepidation due to the inherent reliance on the magic wisdom of the tribe.
DataScienceKit provides APIs for ingesting, processing and transforming data from the point of view of performing analytics.
While business analysts have used business analytics tools for a while to inform their business intelligence needs to inform business priorities , it is only in recent years that academics and data scientists have begun to understand the value of having APIs for consuming the data that they can transform to run their statistical models and regressions on.
The docker image for DataScienceKit can be pulled using docker pull as below
docker pull arunabhdas/netpost-stack-1:latestSource code for DataScienceKit as below
https://github.com/arunabhdas/datasciencekit
