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TigerGraph Unveils ML Workbench, Winners of Its ‘Graph For All Million Greenback Problem’

Graph analytics platform TigerGraph has simply launched its new TigerGraph ML Workbench, a Jupyter-based Python improvement framework.

TigerGraph says this machine studying toolkit “permits information scientists to considerably enhance ML mannequin accuracy, shorten improvement cycles, and ship extra worth to the enterprise whereas utilizing acquainted instruments, workflows, and libraries in a single surroundings that simply plugs into present information pipelines and ML infrastructure.”

The corporate says TigerGraph ML Workbench will permit information scientists to construct deep studying AI fashions utilizing linked information by graph-enabled ML, which they are saying has extra correct predictive energy than a standard ML strategy. That is seen essentially the most in node prediction purposes akin to fraud, and edge prediction purposes like product suggestions. Graph-enhanced ML and graph neural networks, totally built-in with the TigerGraph database, can shortly be used for parallelized graph information processing. The ML Workbench was designed to interoperate with PyTorch, PyTorch Geometric, DGL, and TensorFlow in order that customers can select which deep studying framework they like. TigerGraph additionally says its ML Workbench is plug-and-play prepared for Amazon SageMaker, Microsoft Azure ML, and Google Vertex AI.

“Graph has confirmed to speed up and enhance ML studying and efficiency, however the studying curve to make use of the APIs and libraries to make that occur has confirmed very steep for a lot of information scientists,” mentioned Victor Lee, vp of machine studying and AI at TigerGraph. “So we created ML Workbench to offer a brand new useful layer between the information scientists and the graph machine studying APIs and libraries to facilitate information storage and administration, information preparation, and ML coaching. The truth is, now we have seen early adopters gaining a ten to 50% improve within the accuracy of their ML fashions on account of utilizing ML Workbench and TigerGraph.”

The interface of the TigerGraph ML Workbench. Supply: TigerGraph

Because the ML Workbench is designed to work with enterprise-level information, it’s extremely scalable and can be utilized with very giant graphs. TigerGraph lists ML Workbench’s built-in capabilities as the next: TigerGraph DB’s distributed storage and massively parallel processing, graph-based partitioning to generate coaching/validation/take a look at graph information units, graph-based batching for GNN mini-batch coaching to enhance efficiency and scale back {hardware} necessities, and sub-graph sampling to assist forefront GNN modeling methods.

ML Workbench is suitable with TigerGraph 3.2 and later and is on the market as a totally managed cloud service or for on-prem use. The product is presently in preview and will likely be usually accessible in June 2022. These focused on studying extra can go to this hyperlink.

Along with ML Workbench, TigerGraph additionally unveiled the winners of its Graph for All Million Greenback Problem at its Graph + AI Summit. The corporate awarded $1 million to “game-changing, graph-powered initiatives that analyze and tackle a lot of at the moment’s greatest world social, financial, well being, and climate-related considerations.” Profitable initiatives have been hand chosen by the worldwide judging committee out of 1,500 registrations from over 100 international locations.

The $250K Grand Prize went to Psychological Well being Hero, an utility created to offer elevated entry to customized psychological well being remedy. Different winners embody Biodex, a gamified pure picture identification utility that contributes biodiversity data for analysis, and Diagnosx, a women-led utility that presents a 3D mannequin of a affected person’s analysis and medical historical past and is designed to lower disparities in physician/affected person communication. Go to this hyperlink for a full checklist of winners.

“From addressing psychological well being points to supporting Ukrainian refugees to predicting how provide shocks will unfold by the world economic system, the submissions we obtained deal with real-world considerations and exhibit progressive approaches to resolving them with graph expertise,” mentioned Dr. Yu Xu, founder and CEO of TigerGraph. “We wished to create a problem that produced new and progressive purposes of graph expertise and the worldwide group did simply that. The overwhelming concentrate on world points on this problem reveals that no matter geography, all of us share lots of the similar considerations and challenges, and that graph expertise might help tackle them.”

Associated Objects:

A Million {Dollars} Up for Grabs in TigerGraph Problem

TigerGraph Bolsters Scalability with Graph Database Replace

Graph Databases Gaining Enterprise-Prepared Options



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