SARA ROBINSON: With almost 500 sessions at Next 2018,
it's understandable that you couldn't get to them all.
So if you didn't see my talk, From Zero to ML on Google Cloud
Platform, then stay tuned because here's the rewind.
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Maybe you've never built a machine learning model before.
Or maybe you're a hardcore ML expert.
No matter your level of experience,
we've got something for you on Google Cloud.
In this talk, I covered our spectrum of machine learning
offerings, from products targeted
towards application developers with little
or no machine learning experience
to tools for folks with more ML expertise The talk covered
three of our cloud machine learning products--
Machine Learning APIs, Auto ML, and ML Engine.
Let's get a quick taste of each section,
starting with Machine Learning APIs.
Our ML APIs make it easy to add machine learning to your app
with a single REST API request.
No ML expertise or training data required.
We've got five APIs to help you with common ML tasks--
analyzing images, analyzing video, converting audio
to text, analyzing text, and translating text
into over 100 languages.
I highlighted our Vision and Natural Language APIs
with live demos, showing how you can try these APIs directly
in the browser.
Then, we had a cameo from Nick Hasty, director of R&D
at Giphy, an app for searching and sharing millions of GIFs.
Nick shared how Giphy is using the Vision and Natural Language
APIs to optimize Giphy's search and GIF tagging.
Nick showed us that the ML APIs are a great way
to get up and running with ML quickly.
But what if you need to customize them
with your own data?
That's where our newest addition to Cloud Machine Learning
comes in, Cloud AutoML.
Announced in beta at Next, AutoML
lets you train Vision, Natural Language, and Translation
models with your own data.
No model code required.
In the AutoML section, I showed live demos of each variants
in the AutoML UI.
Starting with Vision, I demoed a model
that can detect the type of cloud in an image.
While you could use the Vision API to tell you
an image has a cloud, we wouldn't
expect it to identify the scientific name of a cloud.
This model can classify five different types of clouds.
AutoML Natural Language lets you build
custom text classification models trained on your own text
data.
I showed a demo using a data set from the nonprofit Donors
Choose, which matches teachers who
need resources for their classroom
with interested donors.
With AutoML NL, I built a model to categorize need requests
from teachers into categories so that donors can easily
be matched with projects they're interested in.
AutoML Translate lets you build domain specific translation
models for translating industry specific jargon
and linguistic nuances.
There are many instances where the Cloud Translation
API will work just fine.
But certain cases require a translation model trained
on your own custom data.
For example, let's say I see the following error message
on my machine.
I can summarize it as the driver is not working.
But what if I need to translate this summary?
In our case, we know this refers to software.
But taken out of context, the translation is ambiguous.
It could be referring to a broken golf club--
the driver club isn't working.
The same sentence could also refer
to a taxi driver who is on strike,
and therefore not working.
A base model wouldn't know which domain this referred to,
and this is where a custom model comes in handy.
I showed a live demo of AutoML Translate using data provided
by our customer we localize.
I'm incredibly excited about AutoML
because it makes custom models accessible to anyone,
regardless of their ML expertise.
But there are cases where you have a custom task
and need more control over the type of model you're using
and the inputs to that model.
That brings us to the third section of the talk--
Cloud ML Engine.
ML Engine is a managed platform for training and serving
your own custom ML models.
It supports models built with TensorFlow, Keras,
Scikit-learn, and XGBoost.
You can leverage ML Engine's GPUs and TPUs to train models,
and you can deploy your models to ML Engine for serving.
To show ML Engine, my teammate Zack Akil
came on stage to share a live demo.
Zack and I each built models independently
to accomplish the same task.
I built one in TensorFlow, and he built one
using Scikit-learn.
We used ML Engine to serve them, and Zack
showed how easy it is to build a front end that
calls different versions of your model,
even if they were built with entirely different frameworks
and dependencies.
Well, that's it.
The talk covered a lot of material.
And I hope it gave you an idea of the variety of ML offerings
we have on Google Cloud.
If you'd like to see the full talk,
check out the link in the description below.
And don't forget to subscribe to the Cloud YouTube
channel for more great Next Rewind content.
Thanks for watching.
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