Analytics Automated

Build Simple Data Pipeline Web Services

A lightweight, web based, workflow management system for turning pipelines of long-running, distributed computation into user driven web servives for Data Science applications.

Documentation Download


Analytics automated, A_A, streamlines the process of turning your predictive models and data analysis pipelines into usable and maintainable web services.

It is trivially easy for Scientists, Researchers, Data Scientists and Analysts to build statistical and predictive models. More often than not these fail to be turned in to useful and usable services; frequently becoming work which does not get actioned futher. In short, organisations often have trouble operationalising the models and insights which emerge from complex statistical research and data science.

With A_A researchers and data scientists can build models in the modelling tool of their choice and then, with trivial configuration, A_A will turn these models in to pipelines with an easy to use API for further integration in to websites and other tools.

A_A is agnostic to the modeling software and technologies you choose to build your group around.

A_A is designed to minimise technology lock-in. Statistical modelling and Data Science expertise is now spread across a wide range of technologies (Hadoop, SAS, R and many more) and such technological proliferation shows no sign of slowing down. Picking a single modeling technology greatly reduces the pool of possible employees for your organisation and backing the "wrong horse" means if you have to change it can be very costly in terms of time, staffing and money.



Build your statistical models in the modelling software of your choice

Install Dependencies

Python3, Redis, Celery, PostgreSQL and Django

Download A_A

Clone the github repo for Analytics Automated

Configure Jobs

...and you're done!


You can download A_A by cloning the github repository.

A_A has a large number of dependencies we strongly advise you read the docs before you get going.

git clone

Alternatively visit the repository, make a fork and help us out!