Collaboration to build national graph database capabilities and talent pool to make graph technology more accessible to startups and enterprises.
The tech community in Singapore – and Asia – can now look forward to attaining the world’s first distributed native graph database training and certification.
TigerGraph and AI Singapore (AISG) have recently signed a Memorandum of Understanding (MOU) to promote Machine Learning (ML) and Artificial Intelligence (AI) industry-enablement activities through AISG’s programmes in Singapore.
This collaboration will offer AISG’s engineers and apprentices graph database training and certification, and jointly develop industry best practices for the deployment of AI and ML models in the cloud and at the edge for Singapore use cases, supporting AISG’s AI Innovation programmes such as 100 Experiments (100E), AI Apprenticeship Programme (AIAP), Makerspace AI Bricks and AI Engineering Hub (AIEH).
TigerGraph is a transformative native parallel graph database that is uniquely conducive for AI and ML applications. Its database structure of nodes and edges creates connecting and traversing links, on which AI and ML depend on to uncover patterns and data to create insights for businesses.
From fraud detection to customer360
The company’s deep link analytics enables it to process terabytes of data and traverse millions of connections in a fraction of a second, making it an ideal solution for critical applications such as fraud detection, customer360, IoT, AI and ML.
Dr Yu Xu, CEO, TigerGraph, said: “Graph technology is really fundamental to AI and ML, where there are a lot of use cases in Asia. We are already seeing huge success in other countries in the areas of anti-money laundering and fraud detection. 8 out of 10 of the largest banking and financial institutions in the world are using graph. We are also meeting this increase in demand from Asia and Singapore.”
Another important use case for AI and ML – customer360 – addresses the need to understand customers, be it for segmentation or upselling, and is critical for decision making across industries whether one is an e-commerce or a brick-and-mortar retailer. “To customers like HBO or Walmart, AI and ML plus graph is going to be in demand because customer journey and customer-centricity are really key to empowering companies to grow,” said Dr Xu.
Serene Keng, Managing Director, Channel and Alliances, APJ, TigerGraph, said: “It is now a business imperative to use AI and ML to drive deep insights to transform operations and improve efficiency and the bottom-line. We are proud to join forces with AISG to help companies realize the full potential of graph database for AI and ML applications through identification and co-development of industry verticals, sharing of best practices and use cases to accelerate adoption of these critical technologies.”
Laurence Liew, Director for AI Innovation, AISG, said: “As a small country with many digital natives and a national AI programme supported by both the public and private sectors, we already punch above our weight in the technological and economic race.”
“With TigerGraph on board, we are excited by the limitless possibilities of graph database in support of AI and ML applications,” Liew added. “For example, new analytics innovation or new graph algorithms may drive superior outcomes to solve AI problem statements presented by the industries in our flagship programme, 100E.”
Looking ahead
AISG and TigerGraph will explore the setting up of a Graph AI Center of Excellence to conduct proof-of-concept and customer projects under the 100E programme.
With regards to the potential ofgraph database in the near future, Dr Xu predicts: “Next year, or rather the next 10 years, is going to be big for graph. In 2022, more investments will made in graph technology.”
He added: “You will see more hardware acceleration news and more innovative applications built on top of graph. It will not be a single innovation; you’ll see a lot of progress and milestones for the graph space across industries.”
