YUFENG GUO: There is a lot of structured data
out there, perhaps more than any other type.
And it's ripe for use with machine learning.
Let's explore a new tool that aims to automatically
build and deploy machine learning models
on structured data.
Will it live up to the promise?
Stay tuned to find out.
Welcome to "AI Adventures," where
we explore the art, science, and tools of machine learning.
My name is Yufeng Guo.
And on this episode, we're going to try out
AutoML tables, one of the newest AutoML products
that Google has added.
AutoML tables enables you to automatically build and deploy
state-of-the-art machine learning models on structured
data faster than you could by hand.
It automates modeling on a wide range
of data types from numbers and classes
to strings, timestamps, lists, and nested fields.
What's interesting about AutoML tables
is that you can do all of this without any coding work.
You just take an exported CSV file, click a few buttons,
wait a while, and you get a very highly tuned model
on the other end.
The team has worked hard to enable the tool
to work for a wide variety of different data types.
And it really explores that vast space of possible models
and hyperparameters to try to optimize things on your behalf.
Let's see what using AutoML tables looks like.
The first step is to import your training data at the Import
tab, give it a name, select the source--
either a table from BigQuery or a file on Google Cloud Storage.
We're going to use an open dataset that
describes marketing outcomes of a Portuguese bank.
And we'll try to model whether a bank deposit will happen.
This first step takes a bit of time
as the system analyzes the columns of your dataset.
Once it's complete, you'll get to edit
the auto-generated schema and select
the column for predicting on.
Here, you can also update the column type, as well as
whether it is nullable or not.
The third tab is called Analyze.
This is a pretty interesting view.
And I think it will be useful for those with datasets
that have a lot of columns to get
a nice overview of their data.
You can even click on the different column names
to see some statistics about your columns.
After analyzing the data, we're ready to do the training.
This is where AutoML really shines because, well, all you
have to do is click Train.
There are some options that you can set,
including a maximum budget of training hours.
This enables you to experiment with your data
if you want and limit that training time before committing
to a full, longer training run.
You'll notice that the training times shown are somewhat
on the long side of things.
And that's because it's not only doing model tuning
but also selecting what model to use in the first place.
So as a result, there's a lot of things
happening during training.
But luckily for us, we don't have to do anything.
Time to just go get lunch or something, I guess.
And we're back.
Now we're onto the fifth tab of AutoML Tables, which
is Evaluation.
If you've used some of the other AutoML products before,
this will look pretty familiar.
You can see how the training did,
as well as metrics about the model's performance.
And finally, in tab number 6, we can deploy the model
to get predictions.
There's even an editor in the browser that will make requests
to your endpoint, so you don't need
to set up a local environment to make these calls to try
and test out your endpoint.
Notice also, there's an option at the top of the page
to toggle between online predictions via the REST API
and batch predictions, which allow you to upload a CSV file
or point to a BigQuery table and get prediction results
on that entire file or table.
Considering the amount of time it takes to use,
AutoML Tables enables you to achieve
a much higher level of model performance
than you could reach manually.
So if you have some structured data
you're thinking of doing some machine learning on,
use this as an excuse to try out AutoML Tables
to see if it's useful for you.
By selecting from state-of-the-art models
and then carefully tuning the models during training,
AutoML Tables can potentially save you a lot of work.
Thanks for watching this episode of "Cloud AI Adventures."
And if you enjoyed it, hit that Like button,
and be sure to subscribe to get all the latest episodes right
when they come out.
For now, check out AutoML tables and harness the insights
in your structured data.
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