Apache Ignite 2.0 release introduced first version of its own distributed Machine Learning (ML) library called ML Grid.
The rationale for building ML Grid is quite simple. Many users employ Ignite as the central high-performance storage and processing systems for various data sets. If they wanted to perform ML or Deep Learning (DL) on these data sets (i.e training sets or model inference) they had to ETL them first into some other systems like Apache Mahout or Apache Spark.
Presently ML Grid supports core distributed algebra implementation based on Ignite co-located distributed processing as well as essential machine learning algorithms such as Linear Regression, Decision Trees, K-Means clustering and more. Future releases will introduce custom DSLs for Python, R and Scala, growing collection of optimized ML algorithms as well support for Ignite-optimized Neural Networks.