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.
The roadmap for ML Grid is to start with core algebra implementation based on Ignite co-located distributed processing. The initial version was released with Ignite 2.0. Future releases will introduce custom DSLs for Python, R and Scala, growing collection of optimized ML algorithms such as Linear and Logistic Regression, Decision Tree/Random Forest, SVM, Naive Bayes, as well support for Ignite-optimized Neural Networks and integration with TensorFlow.
Current beta version of Apache Ignite Machine Learning Grid (ML Grid) supports a distributed machine learning library built on top of highly optimized and scalable Apache Ignite platform and implements local and distributed vector and matrix algebra operations as well as distributed versions of widely used algorithms.