“We cannot solve our problems with the same thinking we used to create them” – Albert Einstein

Autonomio is an augmented intelligence workbench that makes the power of advanced deep learning methodology for the first time accessible to non-programmers. Autonomio utilizes the Tensorflow backend through the Keras library and introduces a new paradigm for ease-of-use with data science workflows that were previously were accessible only by a handful of computer-savvy researchers.

Because the browser based user interface is accompanied by a fully-featured Jupyter Notebook, Autonomio is also suitable for advanced practitioners and introduces a set conveniences to cut down workflow complexity.

Autonomio is a brainchild of Mikko Kotila and Amit Phalsankar, with more than 10 years of individual R&D in natural language processing, machine vision and signals intelligence and close 20 years in data analytics. The codebase is maintained as open-source by Botlab, a non-profit foundation based out of Boston and Helsinki.

read the docs
get the code


The problem of data

Data scientists use a significant fraction of their time in transforming the data in various ways to make its shape and type into an acceptable format for the model. This is a particular headache for inexperienced and less computer-savvy researchers.  Our belief is that the starting use case is being able to puke any data on the model and still get a result that indicates the potential the input signals have. In the command line version, we make it possible to train and implement neural networks to aid humans in decision-making.

The problem of unstructured data

Because most of the data in the world is text, Autonomio has a particular focus on dealing with unstructured data. NLPs have been widely used for a range of purposes and sometimes with a high degree of success. What is not clear from the glorified success stories, is that unstructured data is still largely more of a cost factor than a benefit. By combining word2vec and deep learning, Autonomio makes it possible to train and deploy a state-of-the-art text classification neural network in minutes across a wide range of applications and languages.

The problem of cognitive load

Traditionally data science tools, and especially those related to machine learning, have been inaccessible to most researchers. Tools such as Keras and Tensorflow, that Autonomio depends on, still require significant effort from new users in order to get to a successful result.

Autonomio development thrives towards achieving  industry gold-standard status in three aspects:

>>  Out-of-the-box text classification capability
>>  Flexibility in terms of data ingestion
>>  Minimal cognitive load caused by effective use


0/ Autonomio in a nutshell

Autonomio development efforts are centered around thriving towards achieving industry gold-standard status in three vital aspects

>>  out-of-the-box text classification capability
>>  flexibility in terms of data ingestion
>>  minimal cognitive load caused by effective use

1/ Getting Started

Deep learning with Autonomio is as easy and intuitive as it would be to learn and play an interesting computer game. If data plays an important role in your daily life, it will be better than a game.

>>  Run in a docker container for zero-hassle experience
>>  Option for both Autonomio GUI and Jupyter based use
>>  Available for install through PyPi or Git source

2/ Workflow

>>  Access through GUI or intuitive single-command namespace
>>  Optimized for Notebook use
>>  Train and deploy a neural network in minutes
>>  Never touch code (if you don’t want to)
>>  Access through browser as self-hosted SaaS

3/ Features

>>  TensorFlow backend with Keras abstractions
>>  word2vec based word vectorization
>>  language agnostic classification capability
>>  meaning support for over 10 languages through spaCy
>>  Both ‘x’ and ‘y’ can be text objects (e.g. headlines and category labels)
>>  Integrated plotting with special focus on deep learning outputs