Unlock Better and Faster Predictions with Machine Learning on Connected Data

TigerGraph’s Machine Learning Workbench accelerates the development of graph-enhanced machine learning, to deliver more accurate ML models, more quickly. In-database ML functions in a user-friendly Python framework.

Development Framework for Graph Machine Learning

Enables data scientists to create graph neural network models and graph-enhanced models with production scale data.

Python-level Functions and Capabilities

Prepackaged Python libraries for graph data processing, graph feature engineering, subgraph sampling, data loading, and caching for out-of-DB training.

Compatible with Popular Machine Learning Frameworks

Work with the most popular machine learning frameworks in the market including PyTorch Geometric, DGL, and TensorFlow/Spektral.

Plug-and-Play Ready Machine Learning

Flexible integration paths to works with your existing machine learning infrastructure on Amazon SageMaker, Google Vertex AI, and Microsoft Azure Machine Learning.

“Distributed native property graphs are the best way to manage enterprise data; however, to leverage that data, we need to combine it with machine learning. TigerGraph’s ML tools are the best way to make the most of our systems.”
~ Distinguished Engineer at Fortune 5 Healthcare Company


The Machine Learning Workbench makes it easy for AI/ML practitioners to generate and manage graph features, as well as explore graph neural networks. It is fully interoperable with popular deep learning frameworks:

  • PyTorch Geometric
  • DGL
  • TensorFlow/Spektral

The Machine Learning Workbench is plug-and-play ready for Amazon SageMaker, Google Vertex AI, and Microsoft Azure ML. 


Bigger Business Impact​

  • Yields deeper and better insights with GNNs and graph features to improve model accuracy
  • Increases data scientist productivity
  • Proven across multiple industries

Ease of Development​

  • Python level development
  • Prepackaged functions for graph-based feature generation and data partitioning/sampling
  • Graph data science algorithm library

Seamless Integration​

  • Compatible with PyG, DGL, and TensorFlow
  • Works with your existing machine learning infrastructure on AWS, GCP, and Microsoft Azure

Production Data Scale​

  • Billion node/edges scale
  • Built-in sub-graph sampling improves memory and compute efficiency
  • Leverages TigerGraph’s distribute, massively parallel graph engine

Download the TigerGraph Machine Learning Workbench Datasheet

Explore our Graph Data Science Options

The TigerGraph Machine Learning Workbench is designed to work with enterprise-level data. Users can easily train graph machine learning models even on a large graph without needing a powerful machine thanks to the following built-in capabilities:

    • Native integration with TigerGraph distributed storage and massively parallel processing engine for persisting your connected data and executing graph algorithms with parallel compute.
    • Graph-based partitioning to generate training/validation/testing for supervised graph machine learning models.
    • Graph-based batching for mini-batch training to improve performance and reduce hardware requirements.
    • Sub-graph sampling to support leading-edge graph neural network modeling techniques.
    • Graph-based data loader for link and node prediction applications for both homogeneous and heterogeneous graphs.

The TigerGraph Machine Learning Workbench is compatible with TigerGraph Enterprise Edition v3.2 onwards running on-premises or in the public cloud, right out of the box.

TigerGraph Cloud

Enterprise Edition

Free Community Edition



  • Graph Database: TigerGraph DB compatibility v3.2+
  • Cloud ML Platforms: Amazon SageMaker, Azure ML, GCP Vertex
  • PyG, DGL, TensorFlow ML Framework compatibility

Built-in Python-level Capabilities

  • Graph data partitioning
  • Graph data loading & export (http)
  • Subgraph sampling
  • Data batching
  • Graph feature generation
  • GNN: Node prediction support
  • GNN: Heterogeneous graph support
  • GNN: Link prediction support
  • GNN Inference with real-time data

Installation Options

  • One-click add-on to TigerGraph Cloud
  • Docker image
  • MacOS and Linux installers
  • pip install & conda install

Enterprise Features (Not in Community Edition)

  • Data export from graph DB via both HTTP and Kafka → More reliable and efficient
  • No size limit on data export → Ready for enterprise-level scale
  • Ability to support distributed parallel training → Shorten learning best practices from the experts
  • TigerGraph Professional Support

Unlock better and faster predictions on your connected data using the tools you already know.