Artificial-intelligence and machine-learning models go by the saying, “The more you give, the more you receive.” So, it is not about having the latest and best AI/ML prediction models, but about having a large and high-quality data set. Without enough data to feed an AI/ML model, they become pointless.

Acquiring data becomes one of the most important characteristics in building a solid AI strategy, according to Geoff Tudor (pictured), vice president and general manager, Vizion.ai, at Panzura Inc. Panzura helps users transfer large data sets in multicloud environments, reduce storage, and centralize management. Vision.ai a subdivision of Panzura, helps them make sense of all this data by allowing search, analysis and control capabilities. It uses AI prediction models to give large data sets a valuable meaning.  

“In order to drive the value of machine data, especially when you’re looking at ML and AI … the larger the training data set, the better the prediction models,” Tudor said. 

Tudor spoke with Stu Miniman (@stu), host of theCUBE, SiliconANGLE Media’s mobile livestreaming studio, and guest host John Troyer (@jtroyer), chief reckoner at TechReckoning, during the VMworld event in San Francisco. They discussed Panzura’s services and differentiator, its intersection with VMware, and new announcements (see the full interview with transcript here). (* Disclosure below.)

[Editor’s note: The following answers have been condensed for clarity.]

Miniman: Set the table with us of Panzura today and the value of the sharing app. 

Tudor: Panzura is known predominantly for its file services, of which we can provide a global collaborative namespace across multiple different locations. So … anything where you’re working with a lot of distributed groups that need access to the same kind of working set file. And big-data files have been using Panzura for file services for a number of years. We started to see that the growth of data is not only in user-generated content … but it’s the machine-generated data, and that’s what brought us to Vision.ai. 

Miniman: So what’s the key [intellectual property] that differentiates from others in the marketplace? 

Tudor: So, a couple of years ago, we took some of the core IP that we had … and said, “Let’s build a new cloud-native architecture to manage cloud-native digital machine-generated data.” And [to] be able to transfer that not only for the block storage, but to put in the object storage. So we created something called VBOS, Vizion.ai Block Object Storage, that allows us to index this data and then write it to object but, still, while it’s an object, have it still searchable. And that really unlocks the value of these very large data sets so you no longer have to push this off on a tape or push it off into object storage where it’s no longer available.

Troyer: Are we talking log files? 

Tudor: We’ve created this [VBOS] as a service because in a multicloud world you need one platform where you can ingest these data feeds and these log feeds and then store and [be able to] search for them. People have been generating and deploying on-site log files for some time, but we’ve seen a large interest among our customer base in a hosted service that can securely store and make their logs accessible. 

Miniman: What are some of the typical use cases, outcomes? 

Tudor: So we went into this particular customer [with one of our key partners, phoenixNAP], on-boarded him in five minutes, created the dashboards for him, and now their logs are coming in a number of gigabytes per day. And that can visualize and find out any points of their operations that are creating problems and slow access time for their customers. 

Troyer: We talked about ML and AI, so where does that come into the picture? 

Tudor: The AI and ML aspect of this is because as you get primarily the large data set sizes, then you can start putting machine-generated algorithms on top of it. The first machine-generated analytics that we’ve run on top of it are things such as storage prediction costs. You can drop it down in the infrequent access, and you’re not going to get a higher bill. So we can run these analytics for them, provide that data to them. 

Miniman: VMworld’s talking a lot about multicloud, microservices, and cloud-native. Help us understand the intersection between what you’re doing and how that ties into VMware and their customers? 

Tudor: Traditional log platforms or machine-data platforms, such as Elasticsearch or Splunk, is where you go and you create your architecture and your infrastructure, and you manage that infrastructure as you’re putting that data into it. So it puts the operational burden on the customer to go manage all this. In our view of the world, it needs to be completely serverless. You need to be able to consume machine-data, log-data like a microservice.

We’re kind of trying to turn machine-generated data and democratize it into “simple as a Gmail account.” I request a microservice endpoint, then you write to that endpoint. Now, of course, we’re managing servers, and we’re managing clusters and virtual machines, and all that funness, but it’s transparent to you and, most importantly, you’re not hit with any cost for the infrastructure.  

Troyer: I think you all had an announcement, talk a little about that and how that works with the ecosystem in the audience here. 

Tudor: Yeah, we actually had two announcements … from our file service platform, we’re announcing Vsense certification … so that anywhere you’re running VMware, on any of the cloud providers on top of SAN, vSAN, you now have a file services platform on top of that, that can expand beyond just your NVMe, and also leverage that object storage for this kind of infinite filer for that. And the other announcement we have is the log analytics service. 

Miniman: Tell us a little bit about what are the things that are bringing customers to you?

Tudor: I would think that any data and storage is just a universal problem, and people can’t get enough of it. And, ultimately, they want to get out of the business of managing storage a lot. So in that particular instance, being able to offer them a software-defined file system platform for our traditional filer environment.

In the machine-generated data analytics space with Vision.ai, it is how do I make sense of my data? I need to take all of these data streams and actually put some intelligence on it and create alerts, visualize this data. So our big proposition here is five minutes to visualize your data. The simplicity of it is key, and I think that’s making IT simple to consume, and democratizing is something we’re focused on doing. 

Watch the complete video interview below, and be sure to check out more of SiliconANGLE’s and theCUBE’s coverage of the VMworld event. (* Disclosure: Panzura Inc. sponsored this segment of theCUBE. Neither Panzura nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.) 

Photo: SiliconANGLE