(upbeat music) >> Welcome to our industry drill-down. This is from manufacturing. I'm here with Michael Ger who is the managing director for automotive and manufacturing solutions at Cloudera, and in this first session, we're going to discuss how to drive transportation efficiencies and improve sustainability with data. Connected trucks are fundamental to optimizing fleet performance, costs, and delivering new services to fleet operators, and what's going to happen here is Michael's going to present some data and information, and we're going to come back and have a little conversation about what we just heard. Michael, great to see you. Over to you. >> Oh, thank you, Dave, and I appreciate having this conversation today. Hey, this is actually an area, connected trucks. This is an area that we have seen a lot of action here at Cloudera, and I think the reason is kind of important because first of all, you can see that this change is happening very, very quickly. 150% growth is forecast by 2022, and I think this is why we're seeing a lot of action and a lot of growth is that there are a lot of benefits. We're talking about a B2B type of situation here. So this is truck makers providing benefits to fleet operators, and if you look at the top benefits that fleet operators expect, you see this in the graph over here. Almost 80% of them expect improved productivity, things like improved routing, so route efficiencies and improved customer service, decrease in fuel consumption, but better be, this isn't technology for technology's sake. These connected trucks are coming onto the marketplace because hey, they can provide tremendous value to the business, and in this case, we're talking about fleet operators and fleet efficiencies. So, one of the things that's really important to be able to enable this, trucks are becoming connected because at the end of the day, we want to be able to provide fleet efficiencies through connected truck analytics and machine learning. Let me explain to you a little bit about what we mean by that because how this happens is by creating a connected vehicle analytics machine learning lifecycle, and to do that, you need to do a few different things. You start off, of course, with connected trucks in the field, and you could have many of these trucks 'cause typically, you're dealing at a truck level and at a fleet level. We want to be able to do analytics and machine learning to improve performance. So you start off with these trucks, and the first you need to be able to do is connect to those trucks. You have to have an intelligent edge where you can collect that information from the trucks, and by the way, once you've conducted this information from the trucks, you want to be able to analyze that data in real-time and take real-time actions. Now, what I'm going to show you, the ability to take this real-time action is actually the result of a machine learning life cycle. Let me explain to you what I mean by that. So we have this truck, so we start to collect data from it. At the end of the day, what we'd like to be able to do is pull that data into either your data center or into the cloud where we can start to do more advanced analytics, and we start with being able to ingest that data into the cloud, into the enterprise data lake. We store that data. We want to enrich it with other data sources, so for example, if you're doing truck predictive maintenance, you want to take that sensor data that you've collected from those trucks, and you want to augment that with your dealership service information. Now, you have sensor data and the resulting repair orders. You're now equipped to do things like predict when maintenance will occur. You've got all the data sets that you need to be able to do that. So what do you do here? Like I said, you ingest it, you're storing it, you're enriching it with data. You're processing that data. You're aligning, say, the sensor data to that transactional system data from your repair maintenance systems. You're bringing it together so that you can do two things. First of all, you could do self-service BI on that data. You can do things like fleet analytics, but more importantly, what I was talking to you about before is you now have the data sets to be able to create machine learning models. So if you have the sensor values and the need, for example, for a dealership repair order so you could start to correlate which sensor values predicted the need for maintenance, and you could build out those machine learning models, and then, as I mentioned to you, you could push those machine learning models back out to the edge which is how you would then take those real-time actions I mentioned earlier. As that data that then comes through in real-time, you're running it against that model, and you can take some real-time actions. This analytics and machine learning model, machine learning life cycle, is exactly what Cloudera enables. This end-to-end ability to ingest data, store it, put a query lay over it, create machine learning models, and then run those machine learning models in real-time. Now, that's what we do as a business. Now, one such customer, and I just wanted to give you one example of a customer that we have worked with to provide these types of results is Navistar, and Navistar was kind of an early adopter of connected-truck analytics, and they provided these capabilities to their fleet operators. And they started off by connecting 475,000 trucks, up to well over a million now, and the point here is that they were centralizing data from their telematics service providers, from their trucks' telematics service providers. They're bringing in things like weather data and all those types of things, and what they started to do was to build out machine learning models aimed at predictive maintenance, and what's really interesting is that you see that Navistar made tremendous strides in reducing the expense associated with maintenance. So rather than waiting for a truck to break, and then fixing it, they would predict when that truck needs service, condition-based monitoring, and service it before it broke down so that you can do that in a much more cost-effective manner. And if you see the benefits, they reduced maintenance costs 3 cents a mile down from the industry average of 15 cents a mile down to 12 cents cents a mile. So this was a tremendous success for Navistar, and we're seeing this across many of our truck manufacturers. We're working with many of the truck OEMs, and they are all working to achieve very, very similar types of benefits to their customers. So just a little bit about Navistar. Now, we're going to turn to Q&A. Dave's got some questions for me in a second, but before we do that, if you want to learn more about how we work with connected vehicles and autonomous vehicles, please go to our website, what you see up on the screen. There's the URLs, cloudera.com/solutions/manufacturing, and you'll see a whole slew of lateral and information in much more detail in terms of how we connect trucks to fleet operators who provide analytics, use cases that drive dramatically improved performance. So with that being said, I'm going to turn it over to Dave for questions. >> Thank you, Michael. That's a great example. I love the lifecycle. We can visualize that very well. You've got an edge-use case. You're doing both real time inference, really, at the edge, and then you're blending that sensor data with other data sources to enrich your models, and you can push that back to the edge. That's that life cycle, so really appreciate that info. Let me ask you, what are you seeing as the most common connected vehicle when you think about analytics and machine learning, the use cases that you see customers really leaning into? >> Yeah, that's a great question, Dave 'cause everybody always thinks about machine learning, like this is the first thing you go to. Well, actually it's not. The first thing you really want to be able to do, and many of our customers are doing, is look, let's simply connect our trucks or our vehicles or whatever our IOT asset is, and then you can do very simple things like just performance monitoring of the piece of equipment. In the truck industry, a lot of performance monitoring of the truck, but also performance monitoring of the driver. So how is the driver performing? Is there a lot of idle time spent? What's route efficiencies looking like? By connecting the vehicles, you get insights, as I said, into the truck and into the driver, and that's not machine learning any more, but that monitoring piece is really, really important. So the first thing that we see is monitoring types of use cases. Then you start to see companies move towards more of the, what I call, the machine learning and AI models where you're using inference on the edge, and there you start to see things like predictive maintenance happening, kind of real-time route optimization and things like that, and you start to see that evolution again to those smarter, more intelligent, dynamic types of decision-making. But let's not minimize the value of good old-fashioned monitoring to give you that kind of visibility first, then moving to smarter use cases as you go forward. >> You know, it's interesting. I'm envisioning, when you talked about the monitoring, I'm envisioning you see the bumper sticker how am I driving? The only time somebody ever probably calls is when they get cut off, and many people might think, oh, it's about Big Brother, but it's not. I mean, that's yeah, okay, fine, but it's really about improvement and training and continuous improvement, and then of course the route optimization. I mean, that's bottom-line business value. I love those examples. >> Great. >> What are the big hurdles that people should think about when they want to jump into those use cases that you just talked about? What are they going to run into, the blind spots they're going to get hit with? >> There's a few different things. So first of all, a lot of times, your IT folks aren't familiar with kind of the more operational IOT types of data. So just connecting to that type of data can be a new skill set. There's very specialized hardware in the car and things like that and protocols. That's number one. That's the classic IT-OT kind of conundrum that many of our customers struggle with, but then more fundamentally is if you look at the way these types of connected truck or IOT solutions started, oftentimes the first generation were very custom built, so they were brittle. They were kind of hardwired. Then as you move towards more commercial solutions, you had what I call the silo problem. You had fragmentation in terms of this capability from this vendor, this capability from another vendor. You get the idea. One of the things that we really think that needs to be brought to the table is first of all, having an end-to-end data management platform that's kind of integrated, it's all tested together. You have a data lineage across the entire stack, but then also importantly, to be realistic, you have to be able to integrate to industry kind of best practices as well in terms of solution components in the car, the hardware, and all those types of things. So I think there's, it's just stepping back for a second, I think that there has been fragmentation and complexity in the past. We're moving towards more standards and more standard types of offerings. Our job as a software maker is to make that easier and connect those dots so customers don't have to do it all and all on their own. >> And you mentioned specialized hardware. One of the things we heard earlier in the main stage was your partnership with Nvidia. We're talking about new types of hardware coming in. You guys are optimizing for that. We see the IT and the OT worlds blending together, no question, and then that end-to-end management piece. This is different from, you're right, from IT. Normally everything's controlled, you're the data center, and this is a metadata rethinking, kind of how you manage metadata. So in the spirit of what we talked about earlier today, other technology partners, are you working with other partners to sort of accelerate these solutions, move them forward faster? >> Yeah, I'm really glad you're asking that Dave because we actually embarked on a project called Project Fusion which really was about integrating with, when you look at that connected vehicle lifecycle, there are some core vendors out there that are providing some very important capabilities. So what we did is we joined forces with them to build an end-to-end demonstration and reference architecture to enable the complete data management life cycle. Now, Cloudera's piece of this was ingesting data and all the things I talked about being storing and the machine learning. So we provide that end-to-end, but what we wanted to do is we wanted to partner with some key partners, and the partners that we did integrate with were NXP. NXP provides the service-oriented gateways in the cars. That's the hardware in the car. Wind River provides an in-car operating system that's Linux, that's hardened and tested. We then ran our Apache MiNiFi which is part of Cloudera Dataflow in the vehicle, on that operating system, on that hardware. We pumped the data over into the cloud where we did all the data analytics and machine learning and built out these very specialized models, and then we used a company called Airbiquity once we built those models to do. They specialize in automotive over-the-air updates, so they can then take those models and update those models back to the vehicle very rapidly. So what we said is, look, there's an established ecosystem, if you will, of leaders in this space. What we wanted to do is make sure that Cloudera was part and parcel of this ecosystem, and by the way, you mentioned Nvidia as well. We're working close with Nvidia now so when we're doing the machine learning, we can leverage some of their hardware to get some still further acceleration in the machine learning side of things. So yeah, one of the things I always say about these types of use cases, it does take a village, and what we've really tried to do is build out an ecosystem that provides that village so that we can speed that analytics and machine learning life cycle just as fast as it can be. >> This is, again, another great example of data-intensive workloads. It's not your grandfather's ERP that's running on traditional systems. These are really purpose built. Maybe they're customizable for certain edge-use cases. They're low cost, low power. They can't be bloated, and you're right, it does take an ecosystem. You've got to have APIs that connect, and that takes a lot of work and a lot of thought. So that leads me to the technologies that are sort of underpinning this. We talked a lot on theCUBE about semiconductor technology, and now that's changing, and the advancements we're seeing there. What do you see as some of the key technology areas that are advancing this connected vehicle machine learning? >> You know, it's interesting. I'm seeing it in a few places, just a few notable ones. I think, first of all, we see that the vehicle itself is getting smarter. So when you look at that NXP-type of gateway that we talked about, that used to be kind of a dumb gateway that was really, all it was doing was pushing data up and down and provided isolation as a gateway down from the lower level subsystems, so it was really security and just basic communication. That gateway now is becoming what they call a service oriented gateway, so it can run. It's got discs, it's got memory, it's got all this stuff. So now you could run serious compute in the car. So now, all of these things like running machine learning inference models, you have a lot more power in the car. At the same time, 5G is making it so that you can push data fast enough making low-latency computing available even on the cloud. So now you've got incredible compute both at the edge in the vehicle and on the cloud. And then on the cloud, you've got partners like Nvidia who are accelerating it still further through better GPU-based compute. So I mean the whole stack, if you look at that machine learning life cycle we talked about, no Dave, it seems like there's improvements in every step along the way. We're starting to see technology optimization just pervasive throughout the cycle. >> And then, real quick. It's not a quick topic, but you mentioned security. I mean, we've seen a whole new security model emerge. There is no perimeter anymore in a use case like this, is there? >> No, there isn't, and one of the things that we're- Remember, we're the data management platform, and the thing we have to provide is provide end-to-end lineage of where that data came from, who can see it, how it changed, and that's something that we have integrated into from the beginning of when that data is ingested through when it's stored through when it's kind of processed, and people are doing machine learning. We will provide that lineage so that security and governance is assured throughout the data learning life cycle. >> And federated, in this example, across the fleet. So, all right, Michael, that's all the time we have right now. Thank you so much for that great information. Really appreciate it. >> Dave, thank you, and thanks for the audience for listening in today. >> Yes, thank you for watching. Keep it right there. (upbeat music)

>> Welcome to our industry drill down. This is for manufacturing. I'm here with Michael Ger, who is the managing director for automotive and manufacturing solutions at Cloudera. And in this first session, we're going to discuss how to drive transportation efficiencies and improve sustainability with data. Connected trucks are fundamental to optimizing fleet performance, costs, and delivering new services to fleet operators. And what's going to happen here is Michael's going to present some data and information, and we're going to come back and have a little conversation about what we just heard. Michael, great to see you! Over to you. >> Oh, thank you, Dave. And I appreciate having this conversation today. Hey, you know, this is actually an area, connected trucks, you know, this is an area that we have seen a lot of action here at Cloudera. And I think the reason is kind of important, right? Because you know, first of all, you can see that, you know, this change is happening very, very quickly, right? 150% growth is forecast by 2022 and the reasons, and I think this is why we're seeing a lot of action and a lot of growth, is that there are a lot of benefits, right? We're talking about a B2B type of situation here. So this is truck made, truck makers providing benefits to fleet operators. And if you look at the, the top fleet operator, the top benefits that fleet operators expect, you see this in, in the, in the graph over here, now almost 80% of them expect improved productivity, things like improved routing, right? So route efficiencies, improved customer service, decrease in fuel consumption, better better technology. This isn't technology for technology's sake, these connected trucks are coming onto the marketplace because, hey, it can provide tremendous value to the business. And in this case, we're talking about fleet operators and fleet efficiencies. So, you know, one of the things that's really important to be able to enable us, right, trucks are becoming connected because at the end of the day, we want to be able to provide fleet efficiencies through connected truck analytics and machine learning. Let me explain to you a little bit about what we mean by that, because what, you know, how this happens is by creating a connected vehicle, analytics, machine-learning life cycle, and to do that, you need to do a few different things, right? You start off, of course, with connected trucks in the field. And, you know, you could have many of these trucks because typically you're dealing at a truck level and at a fleet level, right? You want to be able to do analytics and machine learning to improve performance. So you start off with these trucks. And the first thing you need to be able to do is connect to those trucks, right? You have to have an intelligent edge where you can collect that information from the trucks. And by the way, once you collect the, this information from the trucks, you want to be able to analyze that data in real-time and take real-time actions. Now what I'm going to show you, the ability to take this real-time action, is actually the result of your machine-learning lifecycle. Let me explain to you what I mean by that. So we have these trucks, we start to collect data from it, right? At the end of the day what we'd like to be able to do is pull that data into either your data center or into the cloud, where we can start to do more advanced analytics. And we start with being able to ingest that data into the cloud, into that enterprise data lake. We store that data. We want to enrich it with other data sources. So for example, if you're doing truck predictive maintenance, you want to take that sensor data that you've connected, collected from those trucks. And you want to augment that with your dealership, say, service information. Now you have, you know, you have sensor data and the resulting repair orders. You're now equipped to do things like predict when maintenance will work, all right. You've got all the data sets that you need to be able to do that. So what do you do? Like I said, you're ingested, you're storing, you're enriching it with data, right? You're processing that data. You're aligning, say, the sensor data to that transactional system data from your, from your your repair maintenance systems; you're, you're bringing it together so that you can do two things. You can do, first of all, you could do self-service BI on that data, right? You can do things like fleet analytics, but more importantly, what I was talking to you about before is you now have the data sets to be able to do create machine learning models. So if you have the sensor values and the need, for example, for, for a dealership repair, or is, you could start to correlate which sensor values predicted the need for maintenance, and you could build out those machine learning models. And then as I mentioned to you, you could push those machine learning models back out to the edge, which is how you would then take those real-time actions I mentioned earlier. As that data that then comes through in real-time, you're running it again against that model. And you can take some real-time actions. This is what we, this is this, this, this analytics and machine learning model, machine learning life cycle is exactly what Cloudera enables. This end-to-end ability to ingest data; store, you know, store it, put a query lay over it, create machine learning models, and then run those machine learning models in real time. Now that's what we, that's what we do as a business. Now one such customer, and I just want to give you one example of a customer that we have worked with to provide these types of results is Navistar. And Navistar was kind of an early, early adopter of connected truck analytics, and they provided these capabilities to their fleet operators, right? And they started off by, by, you know, connecting 475,000 trucks to up to well over a million now. And you know, the point here is that they were centralizing data from their telematics service providers, from their trucks' telematics service providers. They're bringing in things like weather data and all those types of things. And what they started to do was to build out machine learning models aimed at predictive maintenance. And what's really interesting is that you see that Navistar made tremendous strides in reducing the need, or the expense associated with maintenance, right? So rather than waiting for a truck to break and then fixing it, they would predict when that truck needs service, condition-based monitoring, and service it before it broke down, so that you can do that in a much more cost-effective manner. And if you see the benefits, right, they reduce maintenance costs 3 cents a mile from the, you know, down from the industry average of 15 cents a mile down to 12 cents cents a mile. So this was a tremendous success for Navistar. And we're seeing this across many of our, you know, truck manufacturers. We're, we're working with many of the truck OEMs, and they are all working to achieve very, very similar types of benefits to their customers. So just a little bit about Navistar. Now, we're going to turn to Q and A. Dave's got some questions for me in a second, but before we do that, if you want to learn more about our, how we work with connected vehicles and autonomous vehicles, please go to our web, to our website. What you see up, up on the screen. There's the URL. It's cloudera.com forward slash solutions, forward slash manufacturing. And you'll see a whole slew of collateral and information in much more detail in terms of how we connect trucks to fleet operators who provide analytics. Use cases that drive dramatically improved performance. So with that being said, I'm going to turn it over to Dave for questions. >> Thank you, Michael. That's a great example you've got. I love the life cycle. You can visualize that very well. You've got an edge use case you do in both real time inference, really, at the edge. And then you're blending that sensor data with other data sources to enrich your models. And you can push that back to the edge. That's that life cycle. So really appreciate that, that info. Let me ask you, what are you seeing as the most common connected vehicle when you think about analytics and machine learning, the use cases that you see customers really leaning into? >> Yeah, that's really, that's a great question, Dave, you know, cause, you know, everybody always thinks about machine learning like this is the first thing you go to. Well, actually it's not, right? For the first thing you really want to be able to go down, many of our customers are doing, is look, let's simply connect our trucks or our vehicles or whatever our IOT asset is, and then you can do very simple things like just performance monitoring of the, of the piece of equipment. In the truck industry, a lot of performance monitoring of the truck, but also performance monitoring of the driver. So how is the, how is the driver performing? Is there a lot of idle time spent? You know, what's, what's route efficiency looking like? You know, by connecting the vehicles, right? You get insights, as I said, into the truck and into the driver and that's not machine learning even, right? But, but that, that monitoring piece is really, really important. So the first thing that we see is monitoring types of use cases. Then you start to see companies move towards more of the, what I call the machine learning and AI models, where you're using inference on the edge. And then you start to see things like predictive maintenance happening, kind of route real-time, route optimization and things like that. And you start to see that evolution again, to those smarter, more intelligent dynamic types of decision-making. But let's not, let's not minimize the value of good old fashioned monitoring, that's to give you that kind of visibility first, then moving to smarter use cases as you, as you go forward. >> You know, it's interesting, I'm I'm envisioning, when you talked about the monitoring, I'm envisioning, you see the bumper sticker, you know, "How am I driving?" The only time somebody ever probably calls is when they get cut off it's and you know, I mean, people might think, "Oh, it's about big brother," but it's not. I mean, that's yeah okay, fine. But it's really about improvement and training and continuous improvement. And then of course the, the route optimization. I mean, that's, that's bottom line business value. So, so that's, I love those, those examples. >> Great! >> I wonder, I mean, what are the big hurdles that people should think about when they want to jump into those use cases that you just talked about, what are they going to run into? You know, the blind spots they're, they're going to, they're going to to get hit with. >> There's a few different things, right? So first of all, a lot of times your IT folks aren't familiar with the kind of the more operational IOT types of data. So just connecting to that type of data can be a new skill set, right? There's very specialized hardware in the car and things like, like that and protocols. That's number one. That's the classic IT OT kind of conundrum that, you know, many of our customers struggle with. But then, more fundamentally, is, you know, if you look at the way these types of connected truck or IOT solutions started, you know, oftentimes they were, the first generation were very custom built, right? So they were brittle, right? They were kind of hardwired. Then as you move towards more commercial solutions, you had what I call the silo problem, right? You had fragmentation in terms of this capability from this vendor, this capability from another vendor. You get the idea. You know, one of the things that we really think that we need that we, that needs to be brought to the table, is, first of all, having an end to end data management platform. It's kind of an integrated, it's all tested together, you have a data lineage across the entire stack. But then also importantly, to be realistic, we have to be able to integrate to industry kind of best practices as well in terms of solution components in the car, the hardware and all those types of things. So I think there's, you know, it's just stepping back for a second, I think that there is, has been fragmentation and complexity in the past. We're moving towards more standards and more standard types of offerings. Our job as a software maker is to make that easier and connect those dots, so customers don't have to do it all on all on their own. >> And you mentioned specialized hardware. One of the things we heard earlier in the main stage was your partnership with Nvidia. We're talking about new types of hardware coming in. You guys are optimizing for that. We see the IT and the OT worlds blending together, no question. And then that end-to-end management piece, you know, this is different from, your right, from IT, normally everything's controlled, you're in the data center. And this is a metadata, you know, rethinking kind of how you manage metadata. So in the spirit of, of what we talked about earlier today, other technology partners, are you working with other partners to sort of accelerate these solutions, move them forward faster? >> Yeah, I'm really glad you're asking that, Dave, because we actually embarked on a product on a project called Project Fusion, which really was about integrating with, you know, when you look at that connected vehicle lifecycle, there are some core vendors out there that are providing some very important capabilities. So what we did is we joined forces with them to build an end-to-end demonstration and reference architecture to enable the complete data management life cycle. Now Cloudera's piece of this was ingesting data and all the things I talked about in storing and the machine learning, right? And so we provide that end to end. But what we wanted to do is we wanted to partner with some key partners. And the partners that we did integrate with were NXP. NXP provides the service-oriented gateways in the car, so that's the hardware in the car. Wind River provides an in-car operating system. That's Linux, right? That's hardened and tested. We then ran ours, our, our Apache MiNiFi, which is part of Cloudera data flow, in the vehicle, right on that operating system, on that hardware. We pumped the data over into the cloud where we did the, all the data analytics and machine learning, and built out these very specialized models. And then we used a company called Airbiquity, once we built those models, to do, you know, they specialize in automotive over-the-air updates, right? So they can then take those models, and update those models back to the vehicle very rapidly. So what we said is, look, there's, there's an established, you know, ecosystem, if you will, of leaders in this space. What we wanted to do is make sure that Cloudera was part and parcel of this ecosystem. And by the way, you mentioned Nvidia as well. We're working close with Nvidia now. So when we're doing the machine learning, we can leverage some of their hardware to get some still further acceleration in the machine learning side of things. So yeah, you know, one of the things I, I, I always say about these types of use cases, it does take a village. And what we've really tried to do is build out that, that, that an ecosystem that provides that village so that we can speed that analytics and machine learning lifecycle just as fast as it can be. >> This is, again, another great example of data intensive workloads. It's not your, it's not your grandfather's ERP that's running on, you know, traditional, you know, systems, it's, these are really purpose built, maybe they're customizable for certain edge-use cases. They're low cost, low, low power. They can't be bloated. And you're right, it does take an ecosystem. You've got to have, you know, APIs that connect and, and that's that, that takes a lot of work and a lot of thought. So that, that leads me to the technologies that are sort of underpinning this. We've talked, we've talked a lot on The Cube about semiconductor technology, and now that's changing, and the advancements we're seeing there. What, what do you see as some of the key technology areas that are advancing this connected vehicle machine learning? >> You know, it's interesting, I'm seeing it in, in a few places, just a few notable ones. I think, first of all, you know, we see that the vehicle itself is getting smarter, right? So when you look at, we look at that NXP type of gateway that we talked about. That used to be kind of a, a dumb gateway that was, really all it was doing was pushing data up and down, and provided isolation as a gateway down to the, down from the lower level subsystems. So it was really security and just basic, you know, basic communication. That gateway now is becoming what they call a service oriented gateway. So it can run. It's not, it's got disc, it's got memory, it's got all this. So now you could run serious compute in the car, right? So now all of these things like running machine-learning inference models, you have a lot more power in the car. At the same time, 5G is making it so that you can push data fast enough, making low latency computing available, even on the cloud. So now, now you've got incredible compute both at the edge in the vehicle and on the cloud, right? And, you know, and then on the, you know, on the cloud, you've got partners like Nvidia, who are accelerating it still further through better GPU-based computing. So, I mean the whole stack, if you look at that, that machine learning life cycle we talked about, you know, Dave, it seems like there's improvements in every, in every step along the way, we're starting to see technology optim, optimization just pervasive throughout the cycle. >> And then, you know, real quick, it's not a quick topic, but you mentioned security. I mean, we've seen a whole new security model emerge. There is no perimeter anymore in this, in a use case like this is there? >> No, there isn't. And one of the things that we're, you know, remember we're the data management plat, platform, and the thing we have to provide is provide end-to-end, you know, end-to-end lineage of where that data came from, who can see it, you know, how it changed, right? And that's something that we have integrated into, from the beginning of when that data is ingested, through when it's stored, through when it's kind of processed and people are doing machine learning; we provide, we will provide that lineage so that, you know, that security and governance is assured throughout the, throughout that data learning life's level. >> And federated across, in this example, across the fleet, so. All right, Michael, that's all the time we have right now. Thank you so much for that great information. Really appreciate it. >> Dave, thank you. And thanks for the audience for listening in today. >> Yes, thank you for watching. Keep it right there.