TigerGraph, maker of a graph analytics platform for data scientists, during its Graph & AI Summit event today introduced its TigerGraph ML (Machine Learning) Workbench, a new-gen toolkit that ostensibly will enable analysts to improve ML model accuracy significantly and shorten development cycles. Workbench does this while using familiar tools, workflows, and libraries in a single environment that plugs directly into existing data pipelines and ML infrastructure, TigerGraph VP Victor Lee told VentureBeat. The ML Workbench is a Jupyter-based Python development framework that enables data scientists to build deep-learning AI models using connected data directly from the business. Graph-enabled ML has proven to have more accurate predictive power and take far less run time than the conventional ML approach. Conventional machine learning algorithms are based on the learning of systems by training sets to develop a trained model.

TigerGraph is the world's fastest graph analytics platform designed to unleash the power of interconnected data for deeper insights and better outcomes. We welcome people from all backgrounds who seek the opportunity to help build the next generation graph computing and analytics platform. Are you looking forward to the next generation of AI/ML technologies? Come and join us to push the boundary of ML with data science and cutting-edge graph techniques. TigerGraph's ML/AI department is looking for passionate ML engineers to develop models, systems, and solutions for machine learning with graph data, capitalizing on TigerGraph's massively scalable graph database.

If you think you have a winning idea that enables people to monitor the impact of climate change, predict a global crisis like Covid-19 or solve one of the the world's many big problems today, then you might be interested in TigerGraph's million-dollar challenge that is on now. The Million Dollar Challenge is calling on people to use graph technology, which can link and map out a lot more information than traditional databases, to find answers to these pressing problems. Announced last month, the contest has already drawn 1,000 registrations from 90 countries. Participants include data scientists, developers, product managers, designers, data engineers, machine learning engineers and students. TigerGraph, which offers graph databases for advanced analytics and machine learning, is behind the challenge that promises US$1 million in prize money.

TigerGraph, a company that provides a graph database and analytics software, has expanded its data science library with 20 new algorithms, bringing its total to more than 50 algorithms. Graph databases like TigerGraph have become increasingly popular. They are particularly effective at letting data scientists analyze relationships among millions or billions of entities, and they outperform other types of databases for many deep learning applications. Of course this promising market sees plenty of competitors -- startups like Neo4j, MongoDB, and DataStax, as well as giants like Oracle and Amazon. What sets TigerGraph's product apart is that it is open source, in-database, scalable, and uniquely centered around graph data science. This company is first to offer a distributed native graph database as well, and has gained traction in enterprises.

I am on the ISO GQL standard committee and actively contribute to the GQL graph query language standard. I am writing this blog with my two decades of research (my database Ph.D. program training at the University of Florida) and industry development experience (Microsoft, Oracle, Turn Inc., and TigerGraph) in the database area. I have tried my best to make this technical blog consumable by general readers who are likely to be new to the world of databases. In a nutshell, Neo4j took the LDBC benchmark name and the graph/table schema in LDBC-SNB benchmark, generated its own simplified dummy dataset which is useless in real life (no real correlation between entities, no realistic edge or relationship degrees), cherry-picked 4 simple queries out of the 14 IC queries of LDBC-SNB (page 32) to claim and create the illusion that Neo4j can scale and answer global queries, neither of which is true. A distributed and scalable database doesn't need users to know or care about how many machines or shards the system needs.

The auto manufacturing supply chain is a complex web of suppliers, parts, specialized production lines, tools, and more. It's not an easy task to create a sales forecast and then plan out exactly the materials, parts, supplies, and tools needed to produce automobiles. It gets even more difficult when you throw in an unexpected highly disruptive event such as the COVID-19 pandemic. That's the position Jaguar Land Rover found itself in recently. The company needed to respond quickly when one of its suppliers failed. The company used graph technology to re-sequence how vehicle orders were to be built in the factory.

Free Coupon Discount - Knowledge Graph solution development using TigerGraph, Knowledge Graph Solutions Created by Neena Sathi Preview this Course GET COUPON CODE You will be able to understand and document the use case for knowledge graph solution You will be able to Design a Knowledge Graph solution You will be able to Design / extract data from Knowledge Graph data sources. You will be able to Design / Build key knowledge graph solution components and analytics Finally, You will be able to Prototype a graph analytics experience and document your understanding on Knowledge Graph Insights using a "Rapid Prototyping of Knowledge Graph Solutions using TigerGraph" course will help you strategize knowledge graph use cases and help you build or prototype a use case for your knowledge graph engagement. This course includes - How to define Graph Use Case - How to set up Sandbox using TigerGraph for your Graph use case - How to develop and execute structured graph queries - How to define elastic or higher level graph representation - Finally how to connect your graph solution with other solution components using Python. Who this course is for: Management, strategy and business analyst professionals Architects, technical leads and system analysts from IT organization Senior year undergraduate and graduate students in Business, Analytics, and IT Vendors, consultants and service providers for Graph Analytics 100% Off Udemy Coupon . You will be able to understand and document the use case for knowledge graph solution You will be able to Design a Knowledge Graph solution You will be able to Design / extract data from Knowledge Graph data sources.

TigerGraph today rolled out a new deal that allows customers to store up to 50 GB of data in a distributed graph database running on-premise, matching what it already offered in the cloud. The company also welcomed more than 3,500 attendees to its inaugural Graph AI World conference, which included keynotes from customers like Jaguar Land Rover and UnitedHealth. Banks and healthcare companies have some of the most compelling use cases for graph analytics, including anti-money laundering (AML) and drug discovery. However, these companies are also among the least able to take advantage of cloud-based graph offerings, such as TigerGraph Cloud, due to stringent data regulations. That's the reasoning that went behind TigerGraph's announcement today to give away copies of TigerGraph Enterprise Edition, its full-featured graph database.

Graph + AI World is focused on accelerating AI and machine learning projects with graph algorithms. Join thousands of data scientists, architects, engineers and business executives to improve the world with deeper insights from Graph + AI.

Graph databases, which explicitly express the connections between nodes, are more efficient at the analysis of networks (computer, human, geographic, or otherwise) than relational databases. That gives graph databases a leg up for applications such as fraud detection and recommendation systems. One of the major draws of graph databases is the ability to run graph computational algorithms. These are used for tasks that don't lend themselves well to relational databases, such as graph search, pathfinding, centrality, PageRank, and community detection. Graph algorithms are mostly supported in analytical (OLAP and HTAP) graph databases, although some transactional (OLTP) graph databases such as Neo4j support them.