Unlock Smarter Insights at Scale with Machine Learning on Connected Data

TigerGraph’s Machine Learning (ML) Workbench is a Jupyter-based Python development framework that enables data scientists to quickly build powerful deep learning AI models using connected data. Due to its accurate predictive power, the ML Workbench enables organizations to unlock even better insights and greater business impact on node prediction applications such as anti-money laundering or edge prediction applications such as product recommendations.

Introducing the ML Workbench

The ML Workbench makes it easy for AI/ML practitioners to explore Graph Neural Networks (GNNs) because it is fully integrated with TigerGraph’s database for fast, parallelized graph data processing/manipulation. 

The ML Workbench is designed to interoperate with popular deep learning frameworks such as PyTorch Geometric, DGL, and TensorFlow, providing users with the flexibility to choose a framework they are most familiar with. 

The ML Workbench is also plug-and-play ready for Amazon SageMaker and Google Vertex AI.

Easily Train Graph Neural Networks

The ML Workbench is designed to work with enterprise-level data. Users can easily train GNNs even on a large graph without needing a powerful machine thanks to the following built-in capabilities

  • Sub-graph sampling 
  • Graph data processing for preparing training/validation/test graph data sets
  • Data Batching for GNN training

The ML Workbench is compatible with TigerGraph version 3.2 onwards running on-premises or in the public cloud right out of the box.