Katana Graph raises $28.5 million to handle unstructured data at scale

https://venturebeat.com/wp-content/uploads/2021/02/katana-graph.png?w=2700&strip=all

Original Source Here

Katana Graph, a startup that helps businesses analyze and manage unstructured data at scale, today announced a $28.5 million series A round led by Intel Capital.

Katana Graph was founded by University of Texas at Austin computer science professor Kevash Pingali and assistant professor Chris Rossbach. The company helps businesses ingest large amounts of data into memory, CEO Pingali told VentureBeat in a phone interview. The UT-Austin research group started working with graph processing and unstructured data two years ago and began by advising DARPA on projects that deal with data at scale. Katana Graph works in Python and compiles data using C++.

Like companies that deal with algorithm auditing, AIOps, and model monitoring and management services, a range of startups have emerged to help businesses handle data. Labeled data is the essential ingredient needed to train AI models, which may be why Labelbox raised $40 million earlier this month. Other companies in the area include Datasaur and Scale, which focuses on autonomous vehicle data.

Katana Graph is currently working with customers in health, pharmaceuticals, and security.

“One of the customers we’re engaged with has a graph with 4.3 trillion pages, and that is an enormous amount of data. So ingesting that kind of data into the memory of a cluster is a big problem, and what we were able to with the ingest time is reduce the ingest time from a couple of days to about 20 minutes,” Pingali said.

Today’s round included participation from WRVI Capital, Nepenthe Capital, Dell Technologies Capital, and Redline Capital.

Katana Graph was founded in March 2020 and is based in Austin, Texas. The company has 25 employees and is using the funding to expand its marketing, sales, and engineering teams.

VentureBeat

VentureBeat’s mission is to be a digital town square for technical decision-makers to gain knowledge about transformative technology and transact.

Our site delivers essential information on data technologies and strategies to guide you as you lead your organizations. We invite you to become a member of our community, to access:

  • up-to-date information on the subjects of interest to you
  • our newsletters
  • gated thought-leader content and discounted access to our prized events, such as Transform
  • networking features, and more

Become a member

AI/ML

Trending AI/ML Article Identified & Digested via Granola by Ramsey Elbasheer; a Machine-Driven RSS Bot



via WordPress https://ramseyelbasheer.wordpress.com/2021/02/24/katana-graph-raises-28-5-million-to-handle-unstructured-data-at-scale/

Popular posts from this blog

I’m Sorry! Evernote Has A New ‘Home’ Now

Image
Original Source Here My Apologies! I was critical about the grandfather of all apps — Evernote a few months back when I wrote Why is Evernote losing its Ground? on Medium. With that said, I also wrote another story on Evernote in November of last year — Has Evernote Made A Comeback After Years Of Stagnation? This was the time when Ian Small and his team were making inroads into the technology and strategically making enhancements and revamping the app across all platforms. Evernote Is At A War-front And Wants To Prove A Point. I must h ave written this in my earlier blogs but just to re-iterate — I have been and am an avid user of Evernote since 2009 and is a part of my life for planning my daily life, projects, content writing, and almost everything. Recently, Evernote has launched its HOME or you may call it a DASHBOARD that provides its users a one-stop place to get access to their important and critical notes and also provides a few AI-enabled capabilities that I wil...
Read more

Jensen Huang: Racism is one flywheel we must stop

Image
https://venturebeat.com/wp-content/uploads/2021/07/jensen-huang.jpg?w=1200&strip=all Original Source Here All the sessions from Transform 2021 are available on-demand now. Watch now. Jensen Huang , CEO of Nvidia , received the distinguished lifetime achievement award by the Asian American Engineer of the Year from the Chinese Institute of Engineers (CIE) group. And in his acceptance speech, he made a rare comment beyond Nvidia’s business matters: “Racism is one flywheel we must stop.” The comments came at the end of an acceptance speech where he thanked the group for its award, an annual gathering that recognizes outstanding Asian American scientists, engineers and role models. In a virtual ceremony, Huang was awarded for his contributions as “a visionary and innovator in parallel computing technology that accelerates the realization of AI computing.” He also spoke of his experience as an immigrant and an Asian American. “It is strange to accept a lifetime achievem...
Read more

Fully Explained DBScan Clustering Algorithm with Python

Image
Original Source Here Fully Explained DBScan Clustering Algorithm with Python Unsupervised learning in machine learning on density-based clusters Clustering In unsupervised Learning. A photo by Author In this article, we will discuss the machine learning clustering-based algorithm that is the DBScan cluster. The approach in this cluster algorithm is density-based than another distance-based approach. The other cluster which is distance-based looks for closeness in data points but also misclassifies if the point belongs to another class. So, density-based clustering is suited in this kind of scenario. The cluster algorithms come in unsupervised learning in which we don’t rely on target variables to make clusters. In a cluster, the main concern is on maximum dense connected points. The density-based clustering can be useful in arbitrary shapes even with noise. The working function of the density-based cluster is shown below: We select a random data point. Then it will ...
Read more