>>Welcome back to the cubes coverage here in Washington D. C. For a W. S. Public sector summit. I'm john fraser host of the queues and in person event but of course we have remote guests. It's a hybrid event as well. Amazon is streaming amazon web services, streaming all the teams, some of the key notes of course all the cube interviews are free and streaming out there as well on the all the cube channels and all the social coordinates. Our next guest is Melissa Pavlik President and Ceo POV con joining me here to talk about predictive maintenance, bringing that to life for the U. S. Air Force melissa. Thanks for coming in remotely on our virtual cube here at the physical event. >>Excellent, thank you. Good morning >>Show. People have been been um face to face for the first time since 2019. A lot of people remote calling in checking things out kind of an interesting time, right? We're living in so uh what's your, what's your take on all this? >>Sure. I mean it's a new way of doing business, right? Um I will say, I guess for us as a company we always have been remote so it's not too much of a change but it is definitely challenging, especially trying to engage with such a large user community such as the United States Air Force who isn't always used to working as remotely. So it's definitely a unique challenge for sure. >>Well let's get into, I love this topic. You had a real success story. There is a case study with the U. S. Air Force, what's the relationship take us through what you guys are doing together? >>Sure. Absolutely. So we started working with the Air Force now about five years ago on this subject and predictive maintenance. Sometimes you might hear me catch myself and say CBM plus condition based maintenance. They're synonymous. They mean the exact same thing basically. But about five years ago the Air Force was contemplating how do we get into a space of getting ahead of unscheduled maintenance events? Um if the military they're big push always his readiness how do we improve readiness? So to do that it was a big ask of do we have the data to get ahead of failures? So we started on this journey about five years ago as I said and frankly we started under the radar we weren't sure if it was going to work. So we started with two platforms. And of course when I think a lot of people here predictive maintenance, they immediately think of sensor data and sensors are wonderful data but unfortunately especially in an entity such as the Air Force not all assets are censored. So it also opened up a whole other avenue of how do we use the data that they have today to be able to generate and get ahead of failures. So it did start a really great partnership working not only with the individual, I'll say Air Force entities that Air Force Lifecycle Management Center but we also worked across all the major commands, the individual units, supply control, logistics and everyone else. So it's been a really great team effort to bring together all of those but typically would be rather segregated operations together. >>Yeah, they're getting a lot of props lately on a lot of their projects across the board and this one particular, how did you guys specifically help them modernize and with their and get this particular maintenance thing off the ground? >>Absolutely. I think quite simply it was what really we put their existing data to work. We really wanted to get in there and think about they already had a ton of data. There wasn't a need to generate more. We're talking about petabytes of information. So how do we use that and put it into a focus of getting ahead of failure? We said we established basically three key performance parameters right from the beginning it was, we knew we wanted to increase availability which was going to directly improve readiness. We needed to make sure we were reducing mission aborts and we wanted to get ahead of any kind of maintenance costs. So for us it was really how do we leverage and embrace machine learning and ai paired with just big data analytics and how do we take frankly what is more of a World War Two era architecture and turn it into something that is in the information age. So our modernization really started with how do we take their existing data and turn it into something that is useful and then simultaneously how do we educate the workforce and helping them understand what truly machine learning and AI offer because I think sometimes there's everyone has their own opinions of what that means, but when you put it into action and you need to make sure that it's something that they can take action on, right, it's not just pushing a dot moving numbers around, it's really thinking about and considering how their operations are done and then infusing that with the data on the back end, >>it's awesome. You know the old workflows in the cloud, this is legit, I mean physical assets, all kinds of things and his legacy is also but you want the modernization, I was gonna ask you about the machine learning and ai component, you brought that up. What specifically are you leveraging their from the ai side of the machine learning side? >>Absolutely for us, most and foremost we're talking about responsible a i in this case because unfortunately a lot of the data in the Air Force is human entry, so it's manual, which basically means it's open and rife for a lot of error into that data. So we're really focused heavily on the data integrity, we're really focused on utilizing different types of machine learning because I think on the surface the general opinion is there's a lot of data here. So it would open itself naturally into a lot of potential machine learning techniques, but the reality situation is this data is not human understandable unless you are a prior maintainer, frankly, it's a lot of codes, there's not a whole lot of common taxonomy. So what we've done is we've looked at those supervised and unsupervised models, we've taken a whole different approach to infusing it with truly, what I would say arguably is the most important key element, domain expertise. You know, someone who actually understands what this data means. So that way we can in in End up with actionable output something that the air force can actually put into use, see the results coming out of it. And thus far it's been great. Air mobility command has come back and said we've been able to reduce their my caps, which are parts waiting for maintenance by 18%. That's huge in this space. >>Yeah, it's interesting about unsupervised and supervised machine learning. That's a big distinct because you mentioned there's a lot of human error going on. That's a big part. Can you explain a little bit more because that was that to solve the human error part or was it the mix and match because the different data sets, but why the why both machine learning modes. >>So really it was to address both items frankly. When we started down this path, we weren't sure we were going to find right, We went in with some hypotheses and some of those ended up being true and others were not. So it was a way for us to quickly adjust as we needed to again put the data into actionable use and make sure we were responsibly doing that. So for us a lot of it, because it's human understanding and human error that goes into this natural language processing is a really big area in this space. So for us, adjusting between and trying different techniques is really where we were able to discover and find out what was going to be the most effective and useful in this particular space as well as cost effective. Because for us there's also this resistance, you have to have resist the urge to want to monitor everything. In this case we're talking about really focusing on those top drivers and depending on the type of data that we had, depending on the users that we knew were going to get involved with it as well as I would say, the historical information, it really would help us dictate on supervised versus supervised and going the unsupervised route. In some cases there's just still not ready for that because the data is just so manual. Once we get to a point where there is more automation and more automated data collection, unsupervised will definitely no doubt become more valuable right now though, in order to get those actionable. That supervised modeling was really what we found to be the most valuable >>and that makes total sense. You've got a lot of head room to grow into with Unsupervised, which is actually harder as you as you know, everyone, everyone everyone knows that. But I mean that's really the reality. Congratulations. I gotta ask you on the AWS side what part do they play in all this? Obviously the cloud um their relationship with the Air Force as well, what's their what's their role in this particular maintenance solution? >>Sure, absolutely. And I'll just say, I mean we're really proud to be a partner network with them and so when we started this there was no cloud, so today a lot of opportunity or things we hear about in the Air Force where like cloud one platform one, those weren't in existence, you know, five years ago or so. So for us when we started down this path and we had to identify very quickly a format and a host location that would allow us not only handle large amounts of data but do all of the deep analysis we needed to AWS GovCloud is where that came in. Plus it also is awesome that they were already approved at I. L. Five to be able to host that we in collaboration with them host a nist 801 +71 environment. And so it's really allowed us to to grow and deliver this this impact out over 6000 users today on the Air Force side. So for us with a W. S. Has been a great partnership. They obviously have some really great native services that are inside their cloud as well as the pairing and easy collaboration among not only licensed products but also all that free and open source that's out there because again, arguably that's the best community to pull from because they're constantly evolving and working in the space. But a W has been a really great partner for us and of course we have some of our very favorite services I'm happy to talk about, but they've been really great to work with >>what's the top services. >>So for us, a lot of top services are like ec two's work spaces, of course S three and Glacier um are right up there, but you really enjoy working across glue Athena were really big on, we find a commercial service we're looking for that's not yet available in Govcloud. And we pull in our AWS partners and ask, hey, you know when it's going to get into the gulf cloud space and they move pretty quickly to get those in there. So recent months are definitely a theme in blue. Well, >>congratulations, great solution, I love this application because it highlights the power of the cloud, What's the future in store for the U. S. Air Force when it comes to predictive maintenance. >>Sure. I mean, I think at this point they are just going to continue adding additional top driver analyses you through our work for the past couple years. We've identified a lot of operational and functional opportunities for them. So there's gonna be some definitely foundational changing coming along, some enabling new technologies to get that data integrity more automated as well so that there isn't such a heavy lift on the downstream, we're talking about data cleansing, but I think as far as predictive maintenance goes, we're definitely going to see more and more improvements across the readiness level, getting rid of and eliminating that unscheduled event that drives a lot of the readiness concerns that are out there. And we're also hoping to see a lot more improvement and I'd say enhancement across the supply chain because we know that's also an area that really they could get ahead on your part of our other work as we developed a five year long range supply forecast and it's really been opening some eyes to see how they can better plan, not only on the maintenance side but also supporting maintenance from a logistics and supply, >>great stuff melissa. Great to have you on President Ceo Path Con, you're also the business owner. Um how's things going with the business? The pandemic looks like I'm gonna come out of it stronger. Got the tailwind with cloud technology. The modernization boom is here in, in Govcloud, 10 years celebrating Govcloud birthday here at this event. How's business house? How are you doing >>good. Everything has actually been, we were, I guess fortunate, as I mentioned the very beginning, we were remote companies. So fortunately for us the pandemic did not have that much of an operational hiccup and being that a lot of our clients are in the federal space, we were able to continue working and amazingly we actually grew during the pandemic. We added quite a bit of a personnel to the team and so we're looking forward to doing some more predictive maintenance across, not only explaining the Air Force but the other services as well. >>You know, the people who were Agile had some cloud action going on, we're productive and they came out stronger melissa. Great to have you on the cube. Thank you for coming in remotely and joining our face to face event from the interwebs. Thank you so much for coming on cuba >>All right, thank you, john have a great rest of your day. >>Okay. I'm john for here at the cube with a W. S. Public sector summit in person and remote bringing guest on. We've got the new capability of bringing remotes in. We do in person. I'll see you face to face hear the cube and it's like to be here at the public sector summit. Thanks for watching. Mhm. Mhm >>robert, Herjavec