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TigerGraph ML Workbench Delivers Deep Learning and Graph Neural Network Frameworks for Rapid, Accurate ML and AI Modeling

TigerGraph ML Workbench Delivers Deep Learning and Graph Neural Network Frameworks for Rapid, Accurate ML and AI Modeling
Data Scientists Can Improve Model Accuracy up to 50% by Plugging in the Jupyter-Based Python Development Framework

TigerGraph, provider of a leading graph analytics platform, announced the TigerGraph ML (Machine Learning) Workbench — a powerful toolkit that enables data scientists to significantly improve ML model accuracy, shorten development cycles, and deliver more value to the business, while using familiar tools, workflows, and libraries in a single environment that easily plugs into existing data pipelines and ML infrastructure.

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The ML Workbench is a Jupyter-based Python development framework that allows data scientists to quickly build powerful deep learning AI models using connected data. Graph-enabled ML has more accurate predictive power than the traditional ML approach. The ML Workbench enables organizations to unlock even better insights and greater business value on node prediction applications, such as fraud, and edge prediction applications, such as product recommendations. The ML Workbench makes it easy for AI/ML practitioners to explore graph-enhanced machine learning and 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, 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, Microsoft Azure ML, and Google Vertex AI.

“Graph has proven to accelerate and improve ML learning and performance, but the learning curve to use the APIs and libraries to make that happen has proven very steep for many data scientists,” said Victor Lee, vice president of machine learning and AI at TigerGraph. “So we created ML Workbench to provide a new functional layer between the data scientists and the graph machine learning APIs and libraries to facilitate data storage and management, data preparation, and ML training. In fact, we have seen early adopters gaining a 10 to 50% increase in the accuracy of their ML models as a result of using ML Workbench and TigerGraph.”

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The ML Workbench is designed to work with enterprise-level data. Users can easily train GNNs even on very large graphs due to the following built-in capabilities:

  • TigerGraph DB’s distributed storage and massively parallel processing
  • Graph-based partitioning to generate training/validation/test graph data sets
  • Graph-based batching for GNN mini-batch training to improve performance and to reduce HW requirements
  • Sub-graph sampling to support leading edge GNN modeling techniques

“The main challenge in machine learning is usually not the algorithms. More often, it’s about feeding ML models with the right information, and graphs can be a good means to provide exactly that right information,” said Timo Klimmer, AI/ML cloud solution architect at Microsoft. “Running TigerGraph’s Machine Learning Workbench on Azure helps our customers to leverage the power of graphs and combine it with the power of Azure Machine Learning.”

ML Workbench is compatible with TigerGraph 3.2 onwards, available as a fully managed cloud service and for on-premises use. Currently available as a preview, ML Workbench will be generally available in June 2022.

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