SAM BEDER: Hi, everyone.

My name is Sam Beder, and I'm a product manager

on Android Things.

Today, I'm going to talk to you about Google

services on Android Things, and how

adding these services to your device

can unlock your device's potential.

What I really want to convince you of today

is not only is integrating Google services on Android

Things really, really easy and really, really seamless,

but it can make a huge difference in the use cases

that you can put on your device as well as for your end users.

And I know this year, we have many sessions on Android Things

as well as demos in the sandbox area,

and code labs to learn more about what's

possible on Android Things.

I also know that many of you are coming to this session

already with ideas of devices that you

want to make on Android Things or for IoT devices in general.

And I want to show you today all the compelling use cases

that you can get when you integrate some of these Google

services.

So I'm going to go through a number of services today.

First, I'm going to talk about Google Play services, which

includes a whole suite of tools such as the mobile Vision

APIs, location services, as well as Firebase.

After that, I'm going to dive into Firebase in a little bit

more detail to show you how the real time

database that Firebase provides can

allow you to publish and persist data

and events in interesting ways.

After that, I'm going go into TensorFlow,

and how TensorFlow--

we think-- is the perfect application

of the powerful on-device processing

of your Android Things device to really add intelligence

to that device.

Next, I'm going to talk about Google Cloud platform

and how using Google Cloud platform,

you can train, visualize, and take action

on your devices in the field.

Finally, I'm going to touch on the Google Assistant and all

the amazing use cases that you can

get when you integrate the Google Assistant on Android

Things.

Before I dive into these services,

I want to quickly go over Android Things.

So, Android Things is based on a system on module design.

This means that we work really closely with our silicon

partners to bring you modules which you can place directly

into your IoT devices.

Now, these modules are such that it's

economical to put them in devices when you're

making millions of devices or if you have a very small run,

or if you're just prototyping a device.

So earlier today, we actually had a session

specifically on going from prototype to production

on Android Things, which can give you more detail about how

it's feasible to do all this, all the hardware design,

and bring your device to production on Android Things.

The Android Things operating system

is then placed on top of these modules.

So Android Things is a new vertical

of Android built for IoT devices.

Since we work so closely with our silicon partners,

we're able to maintain these modules in new ways.

It allows these devices to be more secure and updateable.

Also, since it's an Android vertical,

you get all the Android APIs they're

used to for Android development as well as the developer tools

and the Android ecosystem.

In addition, on Android Things we've

added some new APIs such as peripheral iO and user

drivers that allow you to control the hardware

on your device in new ways.

We've also added support for a zero display

build for IoT devices without a screen.

But really the key piece of Android Things, I believe,

is the services on top.

Because of the API surface that Android Things provides,

it makes it much easier for Google

to put our services on top of Android Things.

I say endless possibilities here because not only does Google

already support all the services I'm

going to walk you through today, but any services

that Google makes in the future will be much more portable

on Android Things because of this API surface.

So now, let's start diving into some of these services.

Let's talk about Google Play services and all

the useful tools that it provides.

Google Play services gives you access

to a suite of tools, some of which you see here.

So you get things like the mobile vision APIs,

which allow you to leverage the intelligence in your Android

camera to identify people in an image

as well as faces and their expressions.

You also get the nearby APIs, which lets you--

when you have two devices near each other--

allows those devices to interact with each other

in interesting ways.

You get all the Cast APIs, which lets you

from your Android device cast to a cast enabled device

somewhere else.

Next, you get all the location services,

which lets you query things like,

what are the cafes near me and what are their hours.

You also get the Google Fit APIs,

which allow you to attach sensors and accelerometers

to your device and then visualize

this data as steps or other activities in interesting ways.

Finally, you get Firebase, which we'll

talk about more in a minute.

Some of you might know about CTF certification

and how CTF certification is a necessary step in order

to get these Google Play services.

With Android Things, because of our hardware model

that I just talked about, these modules

actually come pre-certified.

So they're all pre-CTF certified,

meaning Google Play Services will work right out of the box.

You have to do absolutely no work

to get these Google Play services on your Android Things

device.

We also have, for Android Things,

a custom IoT variant of Google Play services.

Now I actually think this is a pretty big deal.

This allows us to make Google Play services more lightweight

by taking out things like phone specific UI elements

and game libraries that we don't think

are relevant for IoT devices.

We also give you a signed out experience

of Google Play services.

So, no unauthenticated APIs because these just aren't

relevant for many IoT devices.

So now, let's dive into Firebase in a little bit more detail.

I'm going to walk you through one of our code samples.

So this is the code sample for a smart doorbell using Firebase.

It involves one of our supported boards,

as well as a button and a camera.

So I'm going to walk you through this diagram.

On the left, you see a user interacting

with the smart doorbell.

What happens is, they press the button on the smart doorbell

and the camera takes a picture of them.

On the right, there's another user

who, in their Android phone, they

can use an app to connect to a Firebase database that

can retrieve that image in real time.

So how does this work?

When you press the button on the smart camera,

the camera takes a picture of you.

Then, using the Android Firebase SDK,

which uses the Google Play services APIs

all on the device, it sends this image

to the Firebase database in the cloud.

The user on the other end can then

use the exact same Google Play services and Android Firebase

SDK on their phone to connect to this Firebase database

and retrieve that image.

In our code sample, we also send this image

to the Cloud Vision APIs to get additional annotations

about what's in the image.

So these annotations could be something like, in this image

there is a person holding a package.

So that can give you additional context about what's going on.

It's pretty cool.

If you actually go and build this demo, you can see.

When you press the button and it takes a picture, in less than a

second the picture will appear.

And then a few seconds later, after the image

is propagated through the Cloud Vision APIs,

the annotations will appear as well.

So to really show you how this works,

I'm going to walk through some of the code that

pushes this data to Firebase.

So the first line you see here is just

creating a new door ring instance

that we're going to use in our Firebase database.

Then, all we need to do to make this data appear

in our Firebase database is set the appropriate fields

of our door ring instance.

So here you can see in the highlighted portion,

we're setting the time stamp and the image fields so that--

with the server time stamp and the image URL--

and then this image as well as the timestamp

will appear in our Firebase database

to be retrieved by the user on the other side.

As I mentioned in our code sample,

we also send our images to the Cloud Vision APIs

to get those annotations.

So, we do that by calling the Cloud Vision APIs

and then simply setting the appropriate field

for those annotations so that that additional context

will appear as well for the user on the other end.

So, Firebase is one of the many Google Play services

that you get with Android Things.

But in the interest of time, I can't talk about

all the Google Play services.

So instead, I want to move on to TensorFlow.

We really think that TensorFlow is the perfect application

for the on device processing of your Android Things device.

So, as you've heard from some of the previous talks on Android

Things, Android Things is not really

well suited if you're just making a simple sensor.

To fully utilize the Android Things platform,

it should be doing more.

There should be some intelligence on this device.

You might wonder, though, if you're making an internet

connected device-- an IoT device--

why do you actually need this on device processing?

There's actually several reasons why

this could be really important.

One reason has to do with bandwidth.

If, for example, you're making a camera that's

counting the number of people in a line

and you just care about that number,

by only propagating out that number

you save huge amounts on bandwidth

by not needing to send the image anywhere.

The second reason for on device processing

has to do with when you have intermittent connectivity.

So if your device is only sometimes connected

to the internet, for it to be really functional

it needs to have on device processing for when

it's offline.

The next reason for on device processing

has to do with the principle of least privilege.

So if you, again, had that camera where all you care about

is the number of people standing in a line,

by the principle of least privilege

you should only be propagating that number

even if you trust the other and where you're sending it.

There's also some regulatory reasons

where this could be important for your use case.

The final reason for device processing

has to do with real time applications.

So if you're, for example, making

a robot that has to navigate through an environment,

you want to have on device processing

so if something comes in front of that robot,

you'll be able to react to the situation.

Again, I want to mention that we have a code lab for TensorFlow