Cloud OnAir: Google Cloud IOT from devices to cloud - enabling business outcomes in a secure way
You. You. To cloud. On air there, are live webinars from. Google cloud every, Tuesday, my. Name is pushkar Sharma I'm a product manager in, Google Cloud IOT, today, we'll be talking about cloud IOT and talking, about the business use cases and, how it applies to IOT all. The way from edge to the cloud. You. Can ask questions any time there are Googlers on standby and, we'll be taking, those questions, at the end of the presentation, but, let's get started. So. Today we will be talking about first, we'll start off with. Measuring. The business outcomes. With IOT then. We'll talk about the, cloud IOT, in general, how, we see it then. We'll talk about our new offerings, cloud IOT edge and edge. TPU, and then, we'll move on to talk about security. And cloud. IOT provisioning. So. Let's talk about business. Outcomes with, IOT. So. There. Was a there, was a survey. Done by McKinsey, and, it. Turns out that 84, percent of the companies, were stuck in pilot phase, for. Over a year and, at. 28 percent for, over two years and, about, thirty percent of these, pilots. Only. Could make it to the scale so about 70 percent of these pilots never made it to the production right. So so, what's really good what's really going on I mean we call this as a pilot purgatory, so essentially, what's going on is you. Started with, pilot. And then, they you never have a. Success. With it so what are the key success metrics, which we found, it. Comes down to really, the, business alignment, you know either are you aligned with your senior executives do they do you have support is there a strong business case or vision, and how it will impact the business or is. There an internal alignment and, how to do, we execute and a long-term. Perspective on. That so. Really it comes down to an ROI if you can establish our Y you. Can easily get these alignments, and that's where you can move from pilot, to. The next stage so what we focus on here, is the, I and not. The how because many times you start up with the technology. Proving. Our POC, whether the technology, works and and. It starts off with a very grassroots thing, but you, want to really try to go down to the. Making a business impact and that's how you're going to move from from. A pilot to the full scale. So. When we when we think about business. Outcomes, you know how you want to really start think about so, it, all starts with digitization. And what is really digitization, is really. A lot of things which are analog, today they. Are they're a your physical blind-spot these. Are things, which are not connected, or even if they there, is a data they are in silos, so, what do you really want to do is you want to connect these, things, customers. Staff. Processes. In a unified manner in, a unified system so. Now you have all this data coming together in, in, single platform and from. Here on now you can start to analyze and calculating, these insights, and these you, know this is your data big.
Data Analytics on, on. Your past, data. Historical, there you can start to see what the, things were. Going on where was the process how things, were happening in yours in your business environment, but. Then you want to also start, to look at can you create a prediction, models, can you use machine learning and advanced. Artificial. Intelligence to. Create predictive. Models, for. For, these, four. For, your business which really, indicative of the key performance, indicators. And then. You want to use all of this information which, is the real-time information coming from the business the, past behavior, past data, and insights. The, predictive, models of what's going to happen in future, to. Really, start to think about workflows. And that's very important, because there are workforce happening today but. They are not connected they are in isolation, you want to think about how can I take that process which is in physical. Form. Happening. Today. Can, we make it. More. Digitized, can we can, we make it all connected, in a seamless way and, that's, where you can start to start optimizing and. And. Get benefit from the, from the from, IOT. So. When when we look at our t ry so it really comes down to that you want to be measuring, ROI. Return on investment. So, there, is a direct ry, which is essentially can with revenues and and cost, you want to increase your revenues can. You sell more or can. You reduce your cost are the things you can do to reduce you said that's that's direct and there are things you can do using IOT, because with with IOT you have more, data more. Insights, so, you can enable features, enable. Sales. Service. An able man you, can do you know improve, pricing, measured. Demand, and elasticity, you can, also you. Know look at the cost and can we improve efficiencies. Uptime, reduce the risk, and. Improve, quality. But. Then there are other, benefits, of. Of. IOT, which you can expand, to, for. Example marketing, and, and this is where, you. Want to know your end-users, better can you use the data to to. Understand, their behavior if they are connected, through IOT, you actually can and, you can then upsell and cross-sell products. And, you. Can do personalization. Based, on that customer you can also you know build better products, because now you understand, the customers, you understand, their behaviors, and and. In generally have a better relationship with the customer, they. Connect with you more you have a better brand on. The. Engagement science, when it comes to service delivery especially. You. Want to have, that deep, customer, knowledge how they use their product, can, we proactively, reach out to them can we help them can. We make their experience very delightful. And. Also, with all the data which is coming through you actually start to understand, a lot of opportunities. Which were in your blind spot and and you. Start to see. How can you use. That data to my, new, businesses, and create new business, outcomes, which. Were not possible. Before. So. Let's, go into few, examples, of different. Verticals, hopefully, this will resonate, with you and give you an idea of how you want to start think about. Different, use cases in IOT, so. For example manufactures, if you think what manufacturing, was really important, I mean important thing in manufacturing, is is like. The is, productivity, the. Factories. Are operational, 24 by 7 can. We keep the uptime can it be more efficient, can. It be more safe because many, of these manufacturing, heavy industries. Are, inherently. Have. Challenges, there because. This is a live environment and, very high temperature, and extreme, environment.
There So. You could use predictive. Maintenance to make sure, we'll. Talk about some of that later on but essentially, use artificial, intelligence, and machine learning, to, create predictive, models for your machines so. Now you have a better understanding, of when these machines is going to fail. So you can take action upfront, you can also understand, what, are the factors which are driving, the. Failures, so you can work on that and extend the life of your of. Your machine so essentially. You're. Not only improving. The asset utilization but, you're also reducing the downtime. The. Second aspect of, manufacturing. Is the is. The process there, it could be inventory. Supply chain or different processes, for example in cars it, goes through paint it, goes through. Retrofitting. Testing and you want to understand how, much time is taking between each process you want to optimize for that and if there is an inventory which is access, piling, up you want to reduce that see, so these are all different processes, you can understand, by using. IOT, and and and getting a real-time insight what's, going on and then. You wanna not, only in the factory but also especially, in just-in-time manufacturing, it, goes beyond the factory it's also connected to your vendors and upstream. And downstream supply, chains and you want, to get, a very good sense of how. Supply. Chain is functioning, is there in going to be any shortages. Of of components. That may impact your production, and also. Delivery maybe customer experience in terms of delivery of these products. And. Any in Journal you, wanna understand. How your your, employees are doing how your machines are doing and can you improve your productivity and, reduce your. Failure, rates and have a high ROI. For. Your customers. There, are many partners, explain. A mile relay or Tammy plus clear, object, these are different partners which are working they, are actually using Google cloud platform to. Deliver that, and. And clearly if you have use cases around that you can also reach, out to us and we, can help that help, on that tier. Likewise. On oil and gas is slightly, different same extreme use. Cases but here the business outcomes are, really around. First. Of all can I get better. Resource. Mapping. Can, I understand where next, set of oil is going to be can. You use machine. Learning and, can I use satellite image image, data to get. That understanding. Also. I want to optimize. The. Machines and and their failure is important, and and and where different assets are the trucks are the real machines and even, employees are you're a surgeon, and this, is where you want to understand are they safe as in a safe environment if, there is going to be a problem you want to be up you.
You, Want to predict that and if there is a problem you want to evacuate, and n the safety is very important, so these are these are the key use. Cases in, you. Know one gas there there, are partners like Cisco los and again, relay or foghorn. They. They operating, in different within. The whole, IOT architecture they operate in some, are in more an edge someone in cloud. But. But. There are a lot of partners. Google. Has where which can help in this these use cases. So. Let's, talk about like logistics, primarily, when we say smart transportation, is, really about, logistics. And that's that, could be vertical by itself but is also feeds if we talk about manufacturing and, even, in retail it feeds the rest, of the industries, as well and here, the. Asset tracking clearly, like these trucks needs to be tracked so as a tracking is important but many times there is also the, product on those, trucks. Which is important, and you want to understand for, example if it's the coals to already understand, how the temperature conditions, are even if you if you're transporting something. Needs to be maintained at certain temperature. Then. The telematics of these, vehicles. How fast they're going are. They following their routes, did they deviate, from their, routes needs. To be notified in real time so you can take action. On that and and then also just like any assets we talked about predictive, maintenance you. Want to understand the the prediction, of. When. These. Assets. Are going to fail can, can we do something to enhance their, life alive, what are the key, failure. Modes. For, these assets. So. Let's. Talk about healthcare, so. We'll talk about healthcare and and pharma and med tech they're two slightly different, but very similar, use. Cases so, healthcare this is about for example hospitals this is where, hospitals. Or extended, daycares where. You're, dealing with patients and really what what the main goal for a, lot of these hospitals in and in, healthcare industry is really to serve. Patients. And the. Patient experience is the most important thing their health is the most important thing so you're using IOT, to first. Of all a. Measure. The assets so there are lots of assets like where the wheelchairs, are where the beds are where, the IV pump is so, so, when there is a need for it customer. The, nurses, don't, have to go look for it and they can address the patient as fast as possible, also. What happens is because, there. Is so, much unpredictability around. These assets, you, end up buying more than you need and. It increases your cost and these are expensive, equipment, you end up having a lot more of inventory. And. And, that adds to your your cost overall and. Then we. Know about this connected health use. Cases around monitoring, of, key. Vitals, of the patients, while they. And. The facility, like, mental page mental, health patients, or. Infants. At such aware you want to make sure either, they don't leave the facility, and. The key vitals, are there if they fall you want to be able to detect follow detection, and take. Appropriate action, as quickly as possible. Then. Let's go into retail. There. Are lots of use. Cases in retail, we are already familiar. With. Many of them we can all associate. With it so. One of the key ones especially for the business comes, down to better, engagement, with the customer because for. Retail the, experience, in the store is the most important thing and can you use the technologies, like beacons, cameras, and geolocation based, on notifications. To. Create, these experiences. And. N, location-based, notifications. And marketing, on, site you may want to also use image. Detection, and video analysis to look at footfall, or you can use our five DS for that and, understand, what, parts of the store are our, high traffic areas, and can, they be used to improve, your promotions, at product placement, and in.
General The. Way you operate your business. Automated. Check outs are also, very important because, it's. Part of the customer experience and. Can, you do. More with IOT with understanding. Who the customer is what products they have what. A counter is associated with to really improve that and, now this is and. Then then there's a lot of use cases not just around customer experience but also the back store operations, and a lot of that is about the best customer, experience but also involved, into entry so, do you have the right amount of inventory at, hand is, their, lack of inventory. Can. You can, you replenish. Quickly. So when customers come looking for something, it. Doesn't run out and then can you also have, a, better understanding, of how your. Shelf, and how the products, are moving on your shelf what. Are the fast mobile products, but, a slow mover can, you optimize their, placement on the on the shop. Let's. Talk about Pharma and met tech so. This is really about, deter. This is about pharmaceutical. Companies and medical technology companies. Who are. In the business of helping. Customers. From. A medication standpoint, and their, main goal is to. Ensure that customers. Are, the. Patients, are taking medicines. Appropriately. And it's not only good for their customers, but also good for the business, where they, can improve. The adherence, of the. Medicines again that really that way they can improve their revenues, but. Also having a better patient, engagement. Not. Only improves the brand but, also allows, them to do automated, interventions. So if a patient. Is not taking medicines. They, can notify. Them or if, it's, connected. Insulin. Meter. Where, you, could measure the insulin levels and inject. Appropriate, amount of insulin to the to the patient that, can be done automated, we as well and. Then, there's a big part about Pharma. In medical is that they spend a lot of time in the FDA process in, the regulatory process and they, have to go through those clinical. Trials which is very expensive and often time they don't a lot of these clinical trials fail, because they just don't have enough sample, points but, with IOT you, can actually improve and accelerate, that because now you can start to get a lot of data live, and. That, will improve the, way you go to market and you, can launch your products faster. Okay, so let's talk about Google. Cloud IOT. What. Do we mean by that so, when we look at Google our cloud IOT platform they're. Really. You. Want to think from both devices all the way to the clouds on the very extreme is the devices, and and. These, are the devices which. Could be edged, device, for example and we have a product we'll talk about shortly is a cloud I already edge which. Allows. You to do three things one is it, allows, you to connect to the Google cloud this. Is a protocol, edge IOT, core which. Does, it securely and I make sure the connection is. Strong and. Easy. For you to develop as a product and. Second. Is the edge ml, which. Is how can, you do inference, on the device into machine learning on the device. And. And. And, create those outcomes without relying on the cloud, we. Do support Android think this is, Google OS. For, IOT, devices which is hardened, built. Specifically, for, IOT. For the best security. But. We also support other operating, systems like embedded devices and, then, X OS as well and, and. Finally. These are the CPUs, or. The processor, units also there is CPU with. The general-purpose or m/c used can be.
GPUs. For, advanced. Machine learning, capabilities. And then we have a dedicated product we just recently launched called edge TPU, which. Is for machine learning inferences. On the, chip itself and it's in a hardware. Accelerator, and we'll talk about it shortly too and on, the right side is the clarity, core, which. Is the entrance point for all the IOT, data into the cloud and this. Is basically, a gateway to the clock inside the cloud for the rest of the products, it, basically pumps, all this data into cloud. Pub/sub and. Which is essentially a message queue and from. There on different. Products, within. Cloud, I. Can. Actually take, up this data for, example you can use cloud functions, to. Create your own applications. You can also use, cloud dataflow to. Do ETL, operations, and transform, the stored data and, store it for example, in bigquery or BigTable. And then use, machine learning to, get. These data from these. Databases. Create. Machine, learning models, and then, do either inference, on the cloud or. You can send this models. Back to the edge ml, to. Create these. Inferences, at the, edge as well and then, you obviously have, the visualization layer, on top of that that's the last thing you could use cloud, data lab data studio or maybe build your own insights, on. Top of the cloud platform to. Create those visualization, charts and reporting. Etc. So that sort of hopefully gives you an idea we, also have a Google, Maps platform, which is also. Google product, which, allows you to get a better location. Updates, and you can feed, that information back. To the. Google cloud platform to. Create for example asset. Tracking, use. Cases on top of that. So. Let's talk about white cloud. Why. Google Cloud IOT, and. What's so differentiated, about it so first of all Android, thing we have a unified, end-to-end integration, with. The seamlessly, it connects with Android things and other OS and connects, to the clouds and makes the development of the hardware, and IOT devices easy it's. A global IOT service it's a purely, a server, less. Service. Where you don't have to maintain different, hubs. You. Simply spin, up your devices you only care or devices, data. Coming through you don't have to worry about scale, and. It, has intelligence built in so it applies. Machine. Learning and AI in, the cloud and. Allows you to create IOT. Which is not simply. Basic. Use cases of for example temperature monitoring, but also create prediction, models, around temperature. As. I. Mentioned it's serverless, by design and, this is true just like bigquery and many of these products, so. It basically is, a global. Network. Backbone. And a front-end which is essentially powering, the billion. Plus user applications. From. Google and pretty, much all, Google. Applications, are built on Google cloud, platform and. Then, using the same network. Backbone, and. So this, is this is what is. Powering, the cloud IOT and you take, the advantage of that. Network for your application, and can build applications with. The same level of reliability and scale. So. Let's talk about cloud IOT edge. So. If you step back, there. Is the edge computing, which is long. Back when it, when, there was no cloud was all. Was. All on Prem but. With there has been a lot of advantages, to, the cloud and people. Been really. Migrating, to the cloud but there are some use cases where, there, is a little bit of edge, which.
Is Still there, and and that's important, from. As various, use cases for example it could be security. It could be that the fact is that you may want you don't want some data at this bottom mostly part of policy and you don't want some part of your data to leave on premises, to the cloud so you can you do a processing on the, on. The edge there. There could be use cases like robotics, where the latency, is very important, so. You don't want, the round-trip delay times to the cloud so you may want to take, certain actions quickly, and. Hence, you want to do edge computing. Likewise. For, predictive maintenance we, talked about a use case about machine learning inference you may actually be doing cloud, learning. By, the ML. Learning in the cloud but. You maybe want to do the, inference, in the in. The device itself. Likewise. Object, recognition so. There here's a here if you want to be taking, a quick for, example it's a manufacturing line and you want to be, testing. For, defects. And and. In, order for you to have the maximum yield and the high throughput you're, going to be doing the fast you know object recognition and again it's an inference, on trim, on. The device itself and. And. That's something you want to do quickly. Likewise. Their use cases we all familiar with smart vehicles. Warehousing. Where, there are a GPS and here. Also you want to be taking actions. Quickly, or maybe the van connectivity. Is not that reliable and hence, you you. Want to do edge computing. So. What. Is a cloud IOT edge. So. As we talked about it extends the clouds data processing, down to the edge it runs, on Android things and other operating, systems, it, connects, to the cloud, using. Cloud. Using. The edge IOT, core, stores. Processes. Filters, drives, intelligence, and then. You can run inferences, for. The tensorflow light models, locally, and it. Allows you to do next-gen of machine, learning at the edge. And. This is how it really works you. Would start, with the building, and the training of the ML model in the cloud you. Aggregate the data coming, for, on the devices you I could get the data you use a machine, learning model to, train it and then you send it back to. You. Essentially would do it you would convert that machine, learning model, from tensorflow to tensorflow light and then, you would compile it for. The. The given target if it is the. GPU. Or general, purpose CPU. Or. If it is edge TP which I'll talk next. You. Won't want to compile it for that platform. And. Then you would install it since, you send it back to the device. And. You will start to do inference, on the on the device itself. So. We talked about HTTP, or several times of what it what really is it so, it is a very tiny. High-performance, inference. Chipset, which. Which. Is journal, which, is not a general purpose CPU it is built custom-built. For machine. Learning it can do fast iterations. And in, its the first phase is to build, for my video and, vision, because these are very high CPU intensive. And. It can run concurrently, about plus 30, plus frames, per second, this. Allows you to create high. Performance, machine learning at the edge, with.
The Low power consumption lower. Footprint. And. With. This not only and. Also the because. It works seamlessly with with, the cloud it's, end-to-end. Implementation. Not. Not isolated, where you have to put, it all together yourself. So you basically use, cloud, IOT core, and. Cloud, Google, cloud for, machine learning and, send these, machine. Learning models back to the the, edge TPU, and you can do inferences, and we are going to provide you the tools and capabilities so, it's it's. Very easy, to use and and deploy. We'll. Also be, providing. With the HTTP. Development kit. It. Will be coming in shortly which essentially, would have an HTP you would have a you know associated, CPU as well would, have a secure element we'll talk about this shortly, it, always. Integrated. On a system is kasam a system on, chip module which, you could use to. To. Create, applications, and. And develop this application before, you go into the full production and. This. Psalm, would actually sit on. Example. A Raspberry Pi. So. That way you could reuse the development boards, you already have in one when do you have and just put. In this new Psalm and which. Is already secured it's, it's connects seamlessly, to cloud, it, has security with built in and. That will allow you to improve. And increase your go-to-market. We. Will also have a USB, connected. HTTP. Oh these are the i-y boards which you can connect to your. To. Your jalapa, for development environment, and then you're going to do machine learning on these as well. Ok. So let's talk about cloud, IOT. Core. And end. This in security, so. So. So the clarity code I mention is is the entrance, point for, the for. All the IOT data well, what what really is it so, it's basically they're two parts to it one is the protocol bridge that's the connectivity, part and that's essentially your connectivity through, MQTT and HTTP. Protocols, it does. Automatic, load balances, takes the data from the, devices and puts it into the pub/sub the, second aspect to it is is this. Entire. Device. Shadow or digital, tuner round like what those devices are how they are managed, so. Essentially all these devices are added, into projects, and registries. And for. Each devices, there is a device ID and and. Additional. Attributes which are required for connection.
So For example will be public key or a certificate and, then. There will be additional metadata, and. State information. About, the device its health, and. Business. Attributes, of these devices but also, the. Telemetry data which, is coming through the devices for example temperature and, humidity, motion and all these different. Sensors. Which are on the devices would be on that. So. One of the very, important, things about what. These devices and IOT in general is security, and read from the google, has a very strong opinion on security. And we've, been pushing the boundaries on that so we actually allow. For the basic idea is that there is we use essentially, a asymmetric. Keys, so, private key is built into the into. The device and the public key of that. Is in the cloud and. The, way it works is that the device. It's. Over, TLS but, the way it works is through, jar tokens so there the device will create a jar token and sign it using the private key and this, private key is then sent it over to the cloud, and. That signature as the private key is not send this the, sign jar T is actually sent and. Jawed. Is signature is verified by the cloud and that's, how the connection is established. We. So, there are problems with that one is that you have to make sure that private key is safe, and secure and what, you would. End up doing is and that's what we recommend is using secure elements from microchip or NXP which, has a private, key built into at the manufacturing, time and then. You use this private key so you, obviously cannot, use. A private key directly so you would have end up having a public key equal and you get that from the manufacturer, and you would. Put that public, key on to the cloud and when the device boots up it does. The same thing it, it. Does job, creation, and signs it and associates, with the with, the cloud now. This. Is a very cumbersome process, where. You have to write a script and and, have these public, key is sent into the, the. Device manager, so what we have, come. Up with and. Try to solve for is with the cloud IOT provision this is a new service. Which. Basically, allows you to, manage. This entire process easily, so to, recap, really, there - problem right so one is this. Key manager you have to secure this keys you, have to transfer these public keys from the SE vendor to the OEM. And then there is the the, device management, part of it is that okay now you have to make sure that. These public, keys and device IDs, are. Pre. Registered, onto the cloud and the same device ID and and, and. The. Target information which project ID and registry ID needs to be actually on the kind of on the form where so, that means that you have to pay a lot of attention it.
Becomes A complex process. So, what we're trying to do here is we're working with silicon. Vendors, selection. Renders and. To get these, public keys from them, which. Was stored you simply use, Real ID or, UDID or, additional. Information for. Security sake to. Get those and claim those devices you, will simply easily create, your targets, which is simply, your. Registries. Where you want to put those devices, in and when the device wakes up, it. Simply, asks for where it needs to go and and. It goes there so, this is exactly what's happening in this slide as. You services, as I said it's the same exact slide as the, previous one but the. Part here is that there is a cloud, IOT provisioning service which, gets the public keys from the silicon vendors and get, and allows, you to claim, them and allows, you to identify, these, devices. And and provide. The related, configuration, and also. Provide an information where these devices need to go as a target, onto the device manager in the cloud and, so when the device wakes up it and all these devices are very generically you don't have no worry about this if I see they're very generic devices they go it go, and is you know basically says okay I'm. XYZ. Where. Am I supposed to go it gets the new config which, tells it where to go and these config could be as elaborate as you want it could be new firmware could be. Anything. You want this device to do and then. It comes back as a new, device and connects. To Google Cloud and that's how it gets. Authenticated. And starts, serving. So. Let's use, that let me I'll do a quick demo, of this, service. And. This is available for early access but. Let me give. You a quick snapshot so for example when I log in as a customer, I'm. Gonna be looking. At a blank screen obviously because there are no elements I have claimed so I'm gonna go in here in this news case I'm using real, IDs so I'm gonna have a real idea real reason, sensually all, all.
The Security elements are chips they're wound up on a tape, and it's is wound up under in a real and it's a package it's essentially the package ID so. I'm going to just put. A package, ID, here. And, I'm gonna just claim it. So. It's going to basically go through these reels, and. And. These. Reels now have shown up, and, these reels, have been claimed but they have not been targeted, so now I'm gonna be. Targeting. Into a registry, so I have, a registry, open here and you. Can see this this is all empty. Right now so I'm going to be putting these devices, in this, registry, and there, is a registry. -. One is the registry, and as in this project, ID and that's exactly what I'm going to be doing here so, I'm going to target it to this. Particular. Test. Target. Here and I'm. Gonna assign, it so essentially what is going to do is move these devices. From. This. Place. To. The to. The. To. That registry, so it's basically what it's doing right now is going through this in the loop. Checking, for. For. These devices and this device is now actually moving into the, into. The registry so when I'm. Gonna try to refresh. Here I. Should. Be able to. See. Shortly. The. Devices should, show up but. If you want to this is available, as, as. Early, access and you. Can sign up for, this early access by clicking onto, this link here. And. You. Know and then we will get back to you we wanted to understand your use cases as well and then we'll, provide you an early, access so. Hopefully it would have come up by now. Ok, so now these devices have come up and. You. Can see all these devices which were empty before and then, it's very easy for us to move. From. These devices and, here, the devices have been empty so now next time you have a next batch of devices, you coming into the exact same process so. You don't have to worry about where the device is when these all devices are very generic when they come up they get their configuration. And then they connect. With. That. We're. Gonna we're, gonna end the, session here, this, is going to be our last slide and then, stay tuned for Q&A and we'll be right back in a few minutes it, may be less than a minute and then we'll. Take up the questions which you may have so you know feel free to give us any questions, any feedback and then, we'll be right back with you thank. You. You. Okay, welcome back so, we looks, like we have three questions here so let's get I get going with the questions so first question is in, terms of cloud ID provisioning, I don't have secure element right now but. Plan, to use it in future products, can I still, use this service. Yes. So we clearly, we are trying to solve for the secure. Element use cases but that doesn't mean that's the only use cases we want to solve for so, we, will be as we. Go through this process we will be solving for the, non secure element use cases because there are ways for you, to create. Public/private, key pairs today, and we want to be able to solve for the for the part where you. Are confident, about, the. Private key but the public key need still needs to be. Connected. And and put that into the cloud repository. And. We want to make that process easy so yes that's a that's a short answer so. Let's see the next question is do. I have to use edge TPU to take advantage, of Google's. Edge. Processing. Okay. So yeah no I mean clearly you know HTTP, gives you that advantage, for. The use cases where you need heavy processing, but there are a lot of edge use cases where maybe, general-purpose CPUs is, perfectly, fine because. Even on the M and now and AI there, is huge range of computational, requirements.
So. You know, we, wouldn't, support. GPU, we will support general-purpose, CPUs. And. You know whatever your model. Is which. You have created and, it can be used through, our, products, to send back those models from clarity, to IOT and from. Cloud, IOT to cloud. IOT edge and. Then you can use these models for, inference, in. The edge and then if if you're going to use TPU then we, will support that that's, not the so HCP is not the only. Thing. We're supporting here. Next. Question we have is can we configure. Cloud IOT edge plus Android things, plus. H TPU. On the currently available raspberry. Pi. Yes. So as I mentioned. It. Is a psalm which is essentially, going to have all these things in in one psalm and. Yes. You could use the Raspberry Pi. With. With that Psalm but. When that Psalm is going to be available sometime, in upcoming months it. Will be available so, you want to sign up for that early access so. Let us know we want to work with you, and, so. Make sure that you are the first customers, but. Yes you could use any general purpose Raspberry Pi and that's the whole idea of their song you just goes on top of it and. You can you, know you start off with your projects right now with our DHT Pio and then when that's available you can. Make. That. HTP. You capable. Okay. Well. Thanks. For all these questions and thanks for your time, stay. Tuned for the, next session, it's. See, each at Google cloud networking, 1 or 2 its routing. And VPC pairing, thank. You again for your time and, you, know feel free to go, to the. Cloud. IOT core I mentioned. There. Was this let. Me see, go, to cloud IOT core website you can sign up for the cloud iid provisioning. Early. Access and also you can sign, up for the HT PU and cloud. IOT edge. Early. Access as well on our website. Ok. Well thanks, again and have, a good day bye. You.