MinHash Based Fuzzy Match on Graph
Many graph use cases require fuzzy matching, a method used to find similar, but not exactly matching, phrases in a database. Some examples of fuzzy matching include inputting a string…
Machine Learning Workbench Propels TigerGraph-powered Machine Learning into the Limelight
When it was announced in May 2022, the TigerGraph Machine Learning Workbench promised to become an essential tool for data scientists who needed to harness the power of graph machine…
Podcast: TigerGraph Machine Learning Workbench with Victor Lee
This transcript is edited from the TigerGraph Connections podcast episode published on June 28, 2022, with Victor Lee, VP of Machine Learning and AI at TigerGraph.Corey Tomlinson: I'm very happy…
TigerGraph 3.5 – Building a Cloud-Native Graph Database Platform
I’m delighted to announce the release of TigerGraph 3.5. This release introduced several improvements, many of which were added thanks to direct feedback from our customers and partners. Included in…
TigerGraph Wraps Momentous 2021, Roars Into 2022
If I had to choose one word to describe 2021, it would be “momentum.” Industries are quickly adopting machine learning and AI workloads for competitive advantage. As one customer told…
TigerGraph Honored for Top Data and AI Vendor in 2021 Datanami Readers’ and Editors’ Choice Awards
TigerGraph Honored for "Top Data and AI COVID-19 Use Case", Also Recognized as "Top Data and AI Vendor" in 2021 Datanami Readers’ and Editors’ Choice Awards We are thrilled to…
Introducing TigerGraph 3.2
TigerGraph Delivers Latest Large-Scale, Enterprise-Grade Graph Solution to Meet Ever-Growing Customer and Market Demand TigerGraph today announced its next major product release, TigerGraph 3.2 — a release that includes more…
