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SACHIN KOTWANI: Let's look at what
the workflow looks like for a typical machine learning
developer today.
First, they would have to, of course,
find a problem that they want to solve using machine learning.
Then they would have to gather data, massage it, split it.
Then they have to find a model that they need to train,
and you might want to tweak that.
Then you'll take the data with a model,
and then you train the model.
You tune it.
You evaluate it.
You do that a bunch of times, and you
need an ML expert or a data scientist
to be able to do that.
Once you're done with that step, you
would give it to someone in DevOps and say, OK here it is.
The model works well.
Please deploy it for me.
And only then can a software developer
start running predictions.
What we want to do is we want to make machine learning just
like the auto mode on your camera.
We want to make it much simpler.
So we want you to pick a problem that you want to solve
or a model, and then we just want you to start using it.
Base APIs within ML Kit allow you to do just that.
While there are ML models under the hood,
they're actually as easy to use as any other APIs.
As you can see, all of the ones listed here
are focused on vision.
But there are more coming soon.
Image labeling API, like many of the other APIs in the ML Kit
is available both on device and in the cloud.
On device, it's free.
You can use it as much as you want.
It's low latency, there's no network connectivity required,
and it does about 400 or so labels.
The cloud version of the API offers
1,000 free calls per month and then it's paid after that.
But it's higher accuracy classification,
so it can do around 10,000 labels.
ISABELLA CHEN: TensorFlow is an open source machine learning
framework for everyone.
And TensorFlow Lite is a lightweight solution
of TensorFlow, which is tailor-made for mobile devices.
So once you have a custom model, the first step
you want to do to make your pretrained model to work better
on mobile is to convert it to TensorFlow Lite.
By doing the conversion, you can make your model smaller
and it runs faster on mobile devices.
And secondly, I want to talk a little bit about Firebase ML
Kit's cloud model hosting.
So one thing I myself really like about Firebase
is it's enabling mobile developers to go serverless.
You don't need to maintain your own servers
taking care of scalability, global availability.
So with ML Kit, in the same spirit,
you can just upload your trained TensorFlow Lite model
to Firebase console, and ML Kit will fully
take care of model hosting, model downloading,
and model updating for you.
And thirdly, with ML Kit cloud model serving feature together
with all those toolings offered by Firebase, doing A/B testing
is really simple if you have multiple versions
of your model.
First of all, you need to create an experiment
for your model name via Firebase Remote Config.
Then in the Remote Config SDK on both Android and iOS,
you can easily get the model name.
You specify the model name was Firebase CloudModelSource
and the register your CloudModelSource
with ModelManager.
From there on, you can leave everything to ML Kit.
We're fully taking care of downloading a model for you,
updating a model for you, and then you
can choose to report performance and your users'
actions via Firebase Analytics.
And lastly, you go to Firebase Console.
And there, you can compare the metrics
of your different versions of models,
how they perform side by side.
A/B testing is just that simple.
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