Sachin Sharma

So far we have read a lot of articles regarding the Convolutional Neural Networks which is a well known method to handle euclidean data structures (like images, text, etc.). However in the real world, we are also surrounded by the non-euclidean data structure like graphs and a machine learning method to handle this type of data domain is known as the Graph Neural Networks.Therefore in this session we will first deep dive into the concepts of Graph ML (Part-1) and during the Hands-on-Session (Part-2) we will build a complete Graph ML application starting from training a GraphSage model till deploying your first Graph ML model on the Nvidia’s Triton Inferencer server. This will be followed by updating amazon product graph (stored in ArangoDB) with predicted node embeddings and then making product recommendation with ArangoDB’s AQL query.