JanusGraph is a scalable graph database optimized for storing and querying graphs containing hundreds of billions of vertices and edges distributed across a multi-machine cluster. It is a project under The Linux Foundation, and includes participants from Expero, Google, GRAKN.AI, Hortonworks, IBM and Amazon.

More info https://janusgraph.org/

OrientDB is an open source NoSQL database management system written in Java. It is a Multi-model database, supporting graph, document, key/value, and object models, but the relationships are managed as in graph databases with direct connections between records.

More info https://orientdb.com/

Azure Cosmos DB is Microsoft’s proprietary globally-distributed, multi-model database service "for managing data at planet-scale" launched in May 2017. It is schema-agnostic, horizontally scalable and generally classified as a NoSQL database.

More info https://azure.microsoft.com/en-us/free/cosmos-db/

The Python Package Index (PyPI) is a repository of software for the Python programming language.

More info https://pypi.org/

Ax is a platform for optimizing any kind of experiment, including machine learning experiments, A/B tests, and simulations.

https://ax.dev/docs/why-ax.html

Apache MXNet is an open-source deep learning software framework, used to train, and deploy deep neural networks.

More info https://mxnet.apache.org/

XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way. The same code runs on major distributed environment (Hadoop, SGE, MPI) and can solve problems beyond billions of examples.

An open source machine learning framework that accelerates the path from research prototyping to production deployment.

More info https://pytorch.org/

Keras (tf.keras) is TensorFlow's high-level API for building and training deep learning models