Workshop: Graph ML, NVIDIA Triton, and ArangoDB: Thinking Beyond Euclidean Space

Sign up for ArangoGraph Insights Platform

Before signing up, please accept our terms & conditions and privacy policy.

What to expect after you signup
You can try out ArangoDB Cloud FREE for 14 days. No credit card required and you are not obligated to keep using ArangoDB Cloud.

At the end of your free trial, enter your credit card details to continue using ArangoDB Cloud.

If you decide that ArangoDB Cloud is not (yet) for you, you can simply leave and come back later.

Graph ML, NVIDIA Triton, and ArangoDB: Thinking Beyond Euclidean Space

Workshop: Graph ML, NVIDIA Triton, and ArangoDB: Thinking Beyond Euclidean Space

So far we have heard a lot about Convolutional Neural Networks (CNNs) which is a well-known method to handle euclidean data structures (like images, text, speech, and time series). However, in the real world, we are also surrounded by non-euclidean data like graphs, and a machine learning method to handle this type of data is known as the Graph Neural Networks. Therefore, in this workshop, we will deepen our knowledge with the concepts of Graph Neural Networks and compare them with CNNs.

This will be followed by an interactive Hands-On Session on Graph ML with a real-world application dataset. At the end, we will deploy our generated Graph ML model on the Triton inference server and perform Graph Analytics with the help of ArangoDB.


  • Introduction to ArangoDB and Nvidia’s Triton Inference server (In Brief)
  • Graph Machine Learning (GML) Motivation
  • Applications of GML from various perspectives
  • Get familiar with the working principles of GraphSage (Inductive Representation Learning algorithm)
  • Train Graph ML (GraphSage) model using PyTorch Geometric library 
  • Dataset: Amazon co-purchasing dataset (OGB-Benchmark)
  • Setting up Triton inference server on a local machine
  • Deploy your first Graph ML model on Triton Server
  • Write down python client-side script to interact with GML model served on Triton
  • NVIDIA Triton meets ArangoDB: Use case for Amazon Product Recommendation
About the Presenter:

Sachin is a Machine Learning Research Engineer at ArangoDB whose aim is to build Intelligent products using thorough research and engineering in the area of Graph Machine Learning. He completed his Masters’s degree in Computer Science with a specialization in Intelligent Systems. He is an AI Enthusiast who has conducted research in the areas of Computer Vision, NLP, and Graph Neural Networks at DFKI (German Research Centre for AI) during his academic career. Sachin also worked on building Machine Learning pipelines at Define Media Gmbh where he worked as a Machine Learning Engineer and Scientist.

Sachin Sharma

Sachin Sharma