open source machine learning and artificial intelligence platform, https://www.h2o.ai

Driverless AI speeds up data science workflows by automating feature engineering, model tuning, ensembling and model deployment. More info https://www.h2o.ai/products/h2o-driverless-ai/

watson-machine-learning-client is a library that allows to work with Watson Machine Learning service on IBM Cloud. Train, test and deploy your models as APIs for application development, share with colleagues using this python library.

See also https://pypi.org/project/watson-machine-learning-client/

This project's goal is the hosting of very large tables -- billions of rows X millions of columns -- atop clusters of commodity hardware. Apache HBase is an open-source, distributed, versioned, non-relational database modeled after Google's Bigtable: A Distributed Storage System for Structured Data by Chang et al. Just as Bigtable leverages the distributed data storage provided by the Google File System, Apache HBase provides Bigtable-like capabilities on top of Hadoop and HDFS.