Fork the World of Real time Learning

ARTML is a high-level Machine Learning API, written in Python and capable of running and building all linear models. It was developed with a focus on enabling continous and real time learning in distributed environments. Current hype is about Deep learning, But the future is deep with real learning. Welcome to the world of Real Learning!

Read the documentation at adaptivemachinelearning.io

Adaptive Real Time Machine Learning (ART-ML)

The term “Real Time” is used to describe how well predictive modeling algorithms can accommodate an ever increasing data load instantaneously. However, such real time problems are usually closely coupled with the fact that conventional data mining algorithms operate in a batch mode where having all of the relevant data at once is a requirement. Thus, here Real Time Machine Learning is defined as having all of the following characteristics, independent of the amount of data involved:

Incremental learning (Learn): Immediately updating a model with each new observation without the necessity of pooling new data with old data.

Decremental learning (Forget): Immediately updating a model by excluding observations identified as adversely affecting model performance without forming a new dataset omitting this data and returning to the model formulation step.

Variable addition (Grow): Adding a new attribute (variable) on the fly, without the necessity of pooling new data with old data.

Variable deletion (Shrink): Immediately discontinuing use of an attribute identified as adversely affecting model performance.

Distributed processing: Separately processing distributed data or segments of large data (that may be located in diverse geographic locations) and re-combining the results to obtain a single model.

Parallel processing: carrying out parallel processing extremely rapidly from multiple conventional processing units (multi-threads, multi-processors or a specialized chip).

Project in PROGRESS...

ARTML Models section is not open source as of now. Will be published soon!

Have any questions? Shoot me an email and I shall get back to you asap!

Email Id: [email protected]

Happy Continual Learning!