Cloud OnAir: Getting Started with Google Cloud IoT

INDRANIL CHAKRABORTY: Hello, everyone Welcome to “Cloud OnAir,” live webinars from Google Cloud We are hosting webinars every Tuesday My name is Indranil Chakraborty I’m the product manager for Cloud IoT Core And we also have– SAMRAT BAIRARIA: Hi My name is Samrat Bairaria I’m a technical program manager at Google Cloud, focusing on IoT Core INDRANIL CHAKRABORTY: And what Samrat and I will be talking about is Google Cloud IoT solution and Cloud IoT Core You can ask any questions any time on the platform And we have Googlers on standby to answer them So let’s get started As a starting point, we need to clarify what we mean by IoT The term is overloaded, as you know But let’s stop and think about it a bit Imagine a city with no congestion at all Can IoT do that? Imagine an airport with no delays Would IoT be able to play a role in that as well? Imagine the perfect energy consumption every day and every place How would IoT make that happen? As in health care, as well, can you imagine medicine that would be tailored with each person? Again, can IoT help with that as well? Like we see with games, but now with physical toys, these are the cues for IoT, connecting the physical world to the cloud, and being able to get a comprehensive view of an ecosystem embedded with those fantastic outcomes IoT is all [INAUDIBLE] Yes But the reality is that IoT is everywhere, and is the key to a better outcome for everyone Now, it is estimated that 8.4 billion devices are connected just in 2017, which is more than 30% higher as compared to the previous year We are in a fast accelerating part of an exponential curve A single jet engine from GE generates about 500 gigabytes of data per flight Two jet engine per plane means 1 terabyte of data How many flights per day? You see where I’m going with this, right? Pratt and Whitney’s geared turbofan fitted with 5,000 sensors generate up to 10 gigabytes of data per second A single twin engine aircraft with an average 12 hour flight time could produce up to 844 terabytes of data To put it into perspective, it was estimated that Facebook accumulated around 600 terabytes of data every day in 2014 But with more than 7,000 engines, Pratt and Whitney could potentially download zettabytes of data once all the engines are in the field Devices are much faster, and they’re constantly generating a large amount of data And we are at a time where we are generating more data than ever before And not only are our devices being connected and generating an extremely large volume of data, it is happening virtually in every industry If we look at manufacturing or industrial, we are seeing impacts to every aspect of business, from connected devices on the shop floor, to connected products in the field From a paper machine, you know that one– and the one which makes diapers, for example, generates about 4 terabytes of data per machine annually Or perhaps a gas or a steam turbine generates a ton of data annually as well In fact, we have partners who are already delivering solutions for manufacturing built on the Google Cloud Platform, which we will talk about as well We are also seeing IoT solutions in health care, whether it is personalized medicine or asset tracking in hospital Someone like a company called Dexcom, which continuously monitors glucose today, and are sending 10k compressed [INAUDIBLE],, which is expected to grow to about 1 terabyte of data per month This is compressed size The extracted data will likely be 10x more than what it sends from the glucose monitor If we look at the transportation industry, for example, we’re seeing significant changes there as well We’re seeing applications in commercial vehicles, to vehicles and the suppliers For example, we’re seeing the use of telematics to improve customer service by responding to early signs of failure in commercial vehicles, and avoiding unexpected breakdowns and towing This use of telematics and maps are, again, generating a ton of data, which can be then processed

to get meaningful insights from the field If we look at consumer products industries, we’re seeing a number of use cases there as well for marketing personalization But recently, we want to deal with a large toy manufacturer, focused on reinventing their business driving innovation and disruption So you get the point When you think about these large volume billions of devices connecting to cloud, or through the internet, it results in generating a massive amount of data from all these devices, which are much larger than what humans have been generating so far So this is IoT data And Google has been dealing with this kind of data for a long time In fact, Google is one of those companies which has seven products which has more than one billion 30 day active users And all of these devices and users are generating a ton of data in petabytes or in exabytes every day And what happens when you collect that massive scale of data? Well, you become a master of big data And you become a data driven company This is our strength This is what Google brings to customers that nobody else can Mastering big data has some benefits We can use the data to get key insights, we can build machine learning models, and so on and so forth It allows you to supercharge innovation, and bring new answers to hard problems We have been working on this for a decade And we have used our knowledge to help our customers and our developers in solving these big data and machine learning problems when you collect this massive scale of data We have worked in the energy sector with our data center optimization We have also worked in many other fields as well When we are talking about IoT, we don’t just focus on one IoT product, or a couple of IoT products We really want to drive the conversation to a business outcome enabled by Google Cloud IoT– analyzing big data in stream or batch, and then applying machine learning capabilities It is together, Cloud IoT as a solution, and under the Cloud IoT umbrella that customers will be supercharged So what does it mean when we say a Cloud IoT solution? Well, as we started talking to our customers, partners, and developers, there were three challenges which appeared And as Google, as a company, we have been dealing with these challenges for a long time First is securely connecting things Security applied to device connectivity, and communication and identity Nobody wants their devices to be hacked And you’ve heard cases in the past where this has happened And you know of the consequences So how do you make sure that all these devices, hundreds of thousands, millions or billions of devices, which are out in the field, have the right security patches, have the right version of their firmware, and are very unlikely to be hacked and compromised? And how do you make sure that all those devices can securely connect to Google Cloud or to other cloud platforms? Second is scale We’re talking about unprecedented data As I just mentioned, engines generate gigabytes of data per day And so when you think about that level of scale, how do you make sure that you can ingest the data at scale from millions of billions of these connected devices? And you can store all this data in a cost effective way And you can analyze and process it in a meaningful way as well And third is actionable insights Once you have this data, what do you do with it? At the end of the day, why does a customer need IoT if he cannot get any meaningful insights? And this is, again, where we have been working with our consumer products and a number of products to understand, to analyze data to get really meaningful insights which we think we can share with the audience at large So, as I mentioned, we have been working for a decade on large scale infrastructure and machine learning And we have built our IoT stack on the same technology and tools that we use at Google, for Google Search, for at least two billion Android devices that connect to Google every day And we have an end-to-end solution to derive intelligence in the moment with Google Cloud IoT Here is an architecture of devices

that can connect to Google Cloud via Cloud IoT Core, which is a new service which I will just mention And you can use our downstream services, such as Pub/Sub, Cloud Function, DataFlow, Cloud BigQuery, machine learning, and other services for storage, processing, analysis, and using as machine learning for predictive insights as well The great thing about our Google Cloud IoT solution is that you can seamlessly move IoT data across Google services And you can decide which services suit you best for your business use case, and then use it for specific use cases You can ingest data with Cloud IoT Core You can distribute data with Cloud Pub/Sub, apply data transformation with Cloud DataFlow, and then store data with Cloud BigQuery, or big cloud storage, or BigTable You can perform ad hoc analysis with Google BigQuery, visualize data using Cloud Data Studio or Datalab, and then derive intelligence using cloud machine learning So let’s start with IoT Core What is Cloud IoT Core? So Cloud IoT Core is a new service which we announced last year And we just announced a general availability of this service It’s a fully managed service that allows you to easily and securely connect your IoT devices to Google Cloud, and then manage and ingest data from millions of global [INAUDIBLE] And we think you would benefit from this service as well So a couple of key benefits of Cloud IoT Core As you know, Google is probably the only non-ISP company who has fiber optic deployment across the Atlantic and across the Pacific Ocean And we have a number of points of presence throughout the globe What this allows us to do is to provide a single global end point for your devices to connect to Cloud IoT Core Whether your devices, or your vehicle, or your asset is in the US, or it’s somewhere in Asia, or somewhere in Africa, you just connect your device to Google Cloud IoT Core And leveraging our global networking infrastructure, we will connect it to the closest point of presence And from there, we’ll connect it to our closest data center So you don’t have to worry about picking which particular server you need to connect to You just connect to one global endpoint for Google Cloud IoT Core And we take it from there Cloud IoT Core has two key components One is what we call Protocol Bridge And second is the Device Manager What’s great about Protocol Bridge is we realized that most of the industrial devices are using standard protocols such as MQTT, and even HTTP in many cases So with Cloud IoT Core, we have native support for MQTT 3.1 and above And what’s great about this is, you can connect your devices without worrying about instantiating a resource, or an infrastructure, or a VM Whether you’re connecting one device, or you’re connecting 10,000, or you’re connecting millions of devices as you get ready for deployment, you just connect your device over MQTT or HTTP, and we internally scale for you So we have automatic load balancing built in as part of Cloud IoT Core managed service And since Cloud IoT Core connects with and publishes its data to Cloud Pub/Sub, you can remotely access all your device data from a central location And you can monitor all your devices or your factories from a central location that way The second component of Cloud IoT Core is Device Manager And this is where we allow users and developers to remotely manage, control, and monitor their devices So, first of all, we really care about security And so we have a provision to individually authenticate each IoT devices We support asymmetry key based authentication, where the public key is stored in with Cloud IoT Core, and the private key is stored with the device And we use the standard [? jar ?] token to sign with the private key And that’s what we use for individually authenticating each device You can use our Device Manager on Cloud IoT Core to update configuration of the devices, and even control the devices You can, for example, send firmware updates,

or new configuration, or new settings remotely from the cloud to the device Your application just needs to call an API And Cloud IoT Core makes sure that those new settings get propagated to all the devices as they come online We also have added row level access to different groups of devices So, for example, you might want to grant only View level access to certain groups of users Or you might want to grant just Add or Delete level access So there are a variety of roles which you can grant by using Cloud IM on Google Cloud IoT Core And you might have heard this term, digital twin And this is something which is used a lot in the industry With Cloud IoT Core, it essentially offers you a digital twin, which is a logical representation of your device on the Google Cloud We also have a rich set of APIs and a UI console for device deployment and monitoring as well So this is, again, a fully managed service with a rich set of APIs which allows you to securely connect, manage, and ingest data at scale from your IoT devices Recently we added a new set of features as well with our recent beta and GA release You can bring your own certificate for additional security And our users can now bring their own device key, which is signed by the certificate authority for device authentication Logical device representation, I just talked about, which is essentially a digital twin on Google Cloud IoT Core And we’ve also added support for HTTP in addition to MQTT as well Now, we understand that many of you, whether you’re a developer, or a device partner, or a customer, before you go full scale deployment, you would want to build some prototypes, some proof of concept to understand the ROI, and to really understand the impact of IoT So in our case, we have a simple UI, User Interface, for management of devices, and monitoring those devices as well So here’s a screenshot of our Google Cloud console, where if you scroll down on the left menu, and you go to IoT Core, you can start sort of adding your devices, and then monitoring them as well So it’s a very simple UI which can really get you started With IoT Core we have a notion of registry, which essentially is a collection of devices within a specific region So you can start by creating a device registry And then the next step, once you create a device registry– for example, here we have created a registry called Weather Station– once you’ve created a registry, you can add devices to it And as you add devices, you would specify the public key for authentication And there are a bunch of other settings which you can add to it as well Once you’ve added and registered a device, when the physical device connects to Google Cloud IoT Core, you would be able to see the status of those devices on the same UI console And with this UI console you’re also able to not just monitor the incoming data, but you’re also able to send configuration or updates to the device from the UI as well So we’ve made sure that we not only can’t handle a massive scale, but we also have a simple UI for you to get started quickly for your initial proof of concept or prototypes So once you’ve used Cloud IoT Core for connecting your devices in a secure way, and then you manage them at scale remotely, you can use Pub/Sub for ingestion and distribution of data The great thing about Pub/Sub is that it has durable message persistence, which means Pub/Sub stores the data which is published to it for seven days So what happens here is as the devices connect to IoT Core, and as they publish data, as they start transmitting data, IoT Core then publishes data from all those devices in a registry to one Pub/Sub topic And Pub/Sub then keeps the data for seven days So even if you lose some connectivity, you still have persistence on that data And as Pub/Sub is a global service, you can use Pub/Sub to remotely monitor to have a central monitoring system for all your globally

dispersed devices In addition, Pub/Sub can be a great tool for distributing data to other downstream services, such as Cloud Function, DataFlow, or even your own ETL pipeline So you can build a simple business logic Let’s take an example If you just want to monitor the incoming temperature data from your temperature sensor, and say every time it goes above 80 degrees Fahrenheit you want to turn on the fan What you can do here is you can write a simple Cloud Function, which subscribes to Pub/Sub And as it gets those temperature readings from the device that’s connected to Cloud IoT Core, your logic will kick in And then you can send a command back to Cloud IoT Core to turn on the fan DataFlow can be used for more complex event processing, similar to how you use Cloud Function And here you can have a window And you can say, I want to compute the median temperature over a 10 minute window or a 10 second window This is great for complex data processing as well And what Pub/Sub allows you to do is it allows you to abstract and separate your upstream devices from your downstream application So you can continue to make changes on your downstream application without meaning to update the library or the firmware of your devices So Cloud IoT Core in conjunction with Pub/Sub provides you a great set of services for securely connecting your devices at scale, ingesting data at scale, and then distributing this data to downstream services for storage, processing, and analysis So that was Cloud IoT Core and how you can build a solution with Google Cloud for IoT And now I’m going to hand it off to Samrat, who is going to give you a demo SAMRAT BAIRARIA: Thank you, Indranil So for this demo which we are about to show you simulates an asset tracking company which can have multiple devices or sensors around the globe I can do it We can just go back quickly So we wanted to showcase how we can build an end-to-end application using Google Cloud IoT Core and other Google Cloud Services, like DataFlow, BigQuery, App Engine, and visualize data on a dashboard in real time I wanted to quickly go over how the back end is set up IoT Core is designed specifically to securely connect, manage, and ingest data from globally dispersed devices We have set up Pub/Sub topics and device registries to connect the devices Once connected, they send the telemetry data via MQTT to the IoT Core endpoint As raw data is arriving into Pub/Sub topics, we then use DataFlow, which subscribes to Pub/Sub topics, set up earlier Cloud DataFlow is a fully managed service for transforming and enriching data streaming in This could be real time or batch data The DataFlow pipeline then dumps the data into BigQuery, which is our data warehouse BigQuery also auto scales And set up is easy All we need to do is specify our data schema to get started So from there, we also have some Cloud Functions set up, which also check the value of the data against Cloud SQL If a certain threshold is crossed, we can then trigger the Cloud Function routine based on alert required Current threshold setups are defined for weather, temperature, and geofence We are also using Cloud Data Store to store geofencing data And in the end, we’re tying all of this information using App Engine In particular, App Engine Flex And we are also using Google Maps API to visualize the data points on the dashboard We are also calling some shipping APIs to know the shipping routes a certain ship might take Now, switching over to the demo So as you can see here, we have built a dashboard where we can see all devices and sensors a typical asset tracking company might have We have the following items on screen We have ships, which are transferring goods around the globe And then we have large containers, which are carrying goods from dockyards into warehouses And then we have trucks out here, which are delivery trucks which are transferring data from the warehouses to a customer, or to a certain asset on the ground One thing to note here is, no matter where the devices are located in the world, you need to not change which endpoint the data is sent to

So you can have devices in the US, in Australia, or even in China You, the developer, do not need to specify an endpoint where the data has to go Google front end will take care of routing the packet to the closest location Let’s drill down a little bit deeper on what kind of information the devices are sending us So in this case, we have a ship And the ships have a bunch of sensors on them And if you can see, this is a passenger fleet out here We have a passenger fleet ship out here And we’re calling shipping APIs to see what is the flag of the ship? And then we have a bunch of sensors on the ship itself, which is streaming data using IoT Core We can see temperature readings We can see the course the ship is taking, as well as the wind speed the ship is encountering All this information is being streamed through IoT Core in real time And we can visualize this information on the dashboard running our App Engine Let’s take another example here of a truck which is en route or which is on a delivery route to a customer So for this truck, again, we have a bunch of sensors on this truck as it’s traveling from point A to point B. We want to track, what is the equipment status? Is it full, or has data been unloaded? We want to make sure of the door status This is for security purposes to see if someone opened the cargo door during transit We also have ambient temperature within the cargo hold And we also have the device battery levels for each device that is on board As the truck is making its journey across from point A to point B, it’s streaming data to us in real time And we can visualize this on screen We can also interact with the devices on these trucks For example, we want to set the threshold of what is the perfect temperature the cargo hold should have So let’s say in this case– let’s take it a little extreme Let’s take it we want it to be one degree Celsius Once you set that, the IoT Core communicates back to the device and tells it, this is a new threshold it needs to act against We also use Cloud Functions in the back end to trigger if certain thresholds are crossed So if you can see on screen, as the threshold has been crossed, it is having a alerts generated on screen You can also see the history of all the data that this device has been streaming All this information is stored into BigQuery We are passing the data from the device, to IoT Core, to DataFlow, and then to BigQuery And then we are acquiring it using the App Engine application You can also interact with other APIs like the Google Maps API, and then make this whole internal application work Let’s take an example out here where we want to separate a geofence And if a device crosses the geofence we want some kind of an alert As you see, if the device is not in a particular geofence, we have a notification on screen So we wanted to show the robustness of IoT Core, and how it interacts with the various of the subsystems underneath, and how fast this whole application works You can also see historical alerts that the devices have been generating and sending it through IoT Core So like Indranil said earlier, it doesn’t matter if you have a few hundred devices, or thousands or devices, or a million devices We scale automatically for you You can seamlessly interact with other Google products, like Google Maps API, and data store, based on data streaming, into IoT Core Hopefully this small demo was able to demonstrate the robustness of IoT Core, and how you can use it for your own business logic, or for your own personal projects Thank you for your time, and over to you, Indranil INDRANIL CHAKRABORTY: Thank you, Samrat Let’s go back to the slides So, as Samrat gave you a demo of asset tracking– and what was great in this demo was you could see real time the movement of the assets And whether it’s in the US or a different location, we can still provide the same real time tracking regardless of the location And it uses IoT Core Google Geo APIs and a bunch of other services where all of this

comes together for a complete solution So the point I want to make here is, Google Cloud IoT is really a platform for end-to-end IoT data processing So for ingestions, connection, and management of your IoT device data, we have IoT Core Cloud Pub/Sub We also have Android Things, which I’m going to talk about in a bit For processing, cleaning, and storing, in a cost effective way for this massive amount of data, we have DataFlow function, Cloud Functions, BigTable, Spanner, and even GCS And, finally, to analyze and visualize, and then predict outcomes, we have BigQuery, Cloud Datalab, Machine Learning Engine, Data Studio And you can even use Cloud Functions and DataFlow So Google Cloud really has all the set of services which you need to build an end-to-end solution, and build some compelling applications, just the way Samrat just showed you about real time asset tracking I want to touch upon Android Things, which is our version of an operating system for the IoT devices Android Things essentially is Android for IoT And whats great about Android Things is it gives you three key benefits One is, since it’s built on Android, it’s highly secure It’s a secured boot operating system And if you have a gateway or a device which runs Android Things, it’s very hard for that device to be compromised Second is it’s fully managed by Google When you have millions or billions of IoT devices globally dispersed, you want to make sure that all those devices have the right firmware version, have the right security patches, and have the same version across all the devices And just the way we do it with our Android mobile phones and Chromebooks, Android Things also ensures that all your fleet of devices will have the same version of operating system and security patches as well So that’s great for a large enterprise, and even a medium sized enterprise customer And finally, Android Things comes with support for some of our Google services, such as TensorFlow and machine learning So you can imagine if you have a camera which runs Android Things and you have a cloud machine learning model, or a TensorFlow model to detect faces of employees of your company, training of the model can happen in cloud The inference can still happen locally on the camera, using Android Things and its set of APIs So it really makes it easy for you to build compelling applications And the benefit of Android Things, the related benefit is since it’s Android, you are tapping into the Android application developer ecosystem as well So it’s really easy to build edge applications for edge compute as well So with Android Things, it’s secure manageability, secure boot, and APIs for machine learning And it works seamlessly with Google Cloud IoT Core We believe we have a pretty compelling solution for not just on the cloud aspect, but also on the device aspect so you can build an end-to-end solution for your business specific use cases We also understand that in order for our platform to be more sort of useful, we can’t just do everything on our own So we’ve been working hard to build our partner ecosystem, both on the device side, as well as on the application side So we have a number of device partners, such as Intel, Wireless, NXP, Arm, Marvell There are a large number of device partners who have IoT devices which work seamlessly with Google Cloud IoT Core So if you’re using any of our partner devices, it’ll work seamlessly with Google Cloud IoT Core We also have a number of SIs and application partners as part of the ecosystem who can help you build your specific application for your IoT use cases, companies such as Mnubo, Agosto, Losant, SOTEC All of these partners can help you build specific IoT applications using Google Cloud Platform and Cloud IoT Core So we are very, very excited about our growing partner ecosystem And this is just a snapshot We have a lot more on the website as well

So why Cloud IoT? And I think I just want to summarize the reason why we think you, as a developer, as a partner, or as a customer should use Google Cloud IoT for your IoT application One, Cloud IoT Core lets developers and enterprise easily connect your millions of devices, which are globally dispersed, through the protocol endpoint, against a global endpoint, with less hassle, and no worries about scale We take care of automatic scaling And that’s great for applications and developers so that you can focus on your business application and not worry about the underlying infrastructure Second is, as part of Google Cloud IoT platform, you’re not just collecting your device data, but connecting your global device network on intelligent cloud Google Cloud machine learning and big data innovations helps you to make sense of the IoT data You can perform ad hoc analysis, as I mentioned, with BigQuery You can visualize data with Data Studio And you can derive meaningful, intelligent insights using Cloud Machine Learning And third is, Cloud IoT Core is a service architecture It’s a fully managed service So you don’t have to instantiate a separate instance of Cloud IoT Core You will just connect your devices to Cloud IoT Core, and off you go with your application So whether you start with few devices as you embark on your journey with the proof of concept, to when you’re ready for deployment and you want to deploy millions, or hundreds of thousands, or billions of devices, it’s the same application It’s the same service which you just connected to without worrying about the underlying infrastructure Fourth is, given our global network, we can really offer minimal latency And especially in the case of IoT, every millisecond, every second matters And latency is critical And Google ensures that your devices are delivered with the lowest latency using our global networking infrastructure The highest quality of private networks that connects our regional locations and data centers to more than 100 global network points of presence close to your device and your users This means that the device will always connect to the closest point of presence and benefit from our global network backbone with the lowest latency And finally, Cloud IoT core works seamlessly with millions of Android Things devices, and devices from leading hardware makers, manufacturers, such as Intel, NXP, Microchip, Marvell, Sierra Wireless, and others And the Android Things device operating system is updated and passed by Google As data is generated in the sensor, it seamlessly moves into Google Cloud for processing, analyzing, and integration So that was an overview and a demo for building IoT solutions on Google Cloud Thank you for your time And stay tuned for live Q&A We will be back in less than a minute Thank you everyone for the questions

that we received from the audience Samrat and I are going to walk you through and try to answer as many as we can So let’s get started So the first question we had was, what are some of the advantages of MQTT over HTTP? So there are a couple of advantages First of all, what we find is that in the industry, many of the devices are already using MQTT And MQTT, as you know, is a relatively new format which was introduced in, I think, towards the end of the 1990s The key benefits are, with MQTT, it’s built over TCP So you can use TLS and achieve the same level of security as you do with HTTP In addition, with MQTT, the payload size, the bandwidth consumption is much lower You can send binary data over MQTT And that saves you a lot in bandwidth So that is one of the key reasons why many of those devices use MQTT SAMRAT BAIRARIA: And you don’t need much storage space with MQTT So you can have a smaller footprint for a device INDRANIL CHAKRABORTY: That’s right You don’t need much storage space And then second is MQTT has this topic structure which really works well for devices, as devices publish data on a particular topic, and you have applications subscribing to those topics to collect data And you can also use it for device to device communication as well So I think those are the two key benefits of MQTT over HTTP SAMRAT BAIRARIA: [INAUDIBLE] INDRANIL CHAKRABORTY: Second question What formats are supported for payload? So the way we look at IoT Core is, we’ve taken a set of data agnostic and format agnostic approach At the same time, our recommendation is, you can use JSON as a format You can also use Protobuf If bandwidth is critical for you, and you want to save every bit, you can use Protobuf as well to send binary data Third question is, do I have to create keys for devices on the cloud? You want to take that? SAMRAT BAIRARIA: Yeah Well, you can use keys, or you can also use certificates We have a bring your own certificate mechanism where you can generate your own certificate Give us the public certificate on IoT Core And you can have your device assigned with your private certificate So you can choose whatever path you want to take You can create your keys on Cloud IoT Core You can do it using Open SSL on your machine Or you can get your own certificates INDRANIL CHAKRABORTY: Yeah And I think, if I understand it right, you really want to create virtual devices on cloud for the purpose of testing And the short answer is, yes Even if you are creating virtual devices to connect to Cloud IoT Core, you have to use private keys and public keys, a private-public key combination to connect to Cloud IoT Core Otherwise it will reject connection And on our website, there are very clear instructions and samples which will help you to generate keys, whether it’s for a virtual device, or even for a physical device Then the final question is, how can I use multiple MQTT topics? So there are two ways to address this question One is, if you’re trying to use multiple topics on the device side, today we said we had two parent level topics So one for sending telemetry, which are telemetry events, and one for the device to publish its own state information And for telemetry, the topic is slash events Devices slash device ID, slash events But you can create subfolders So if you want to send temperature on a separate topic, you can say slash event, slash temperature, or slash event, slash pressure And interesting enough, just now in our GA release we also have a new feature which will allow you to map these different MQTT topics to a different Pub/Sub topic on Google Cloud So you can map the temperature topic to temperature Pub/Sub, to [INAUDIBLE] the topic, and then so on and so forth So you can really use multiple topics, both on the device side, as well as on the cloud side Great So thanks everyone, again, for the time And hopefully this was useful And we love to hear your feedback Stay tuned for the next session, End-to-End Machine Learning with TensorFlow on the Google Cloud Platform, live from our Kirkland office And thanks again SAMRAT BAIRARIA: Thank you everyone