YUFENG GUO: Are you looking to do some hyperparameter tuning
on your machine-learning model?
Or perhaps you have other parameters
you'd like to be optimizing in your life.
Then you've got to check out AI Platform Optimizer, one
of the newest products from the Google AI Team.
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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 dive
into the world of optimization with AI Platform Optimizer.
The AI Platform Optimizer was built
to optimize parameters of machine-learning models.
So you can optimize things like learning
rate, batch size, and other typical machine-learning
hyperparameters.
This is the most common use case for Optimizer today.
But wait.
There's more.
You can choose any arbitrary values to tune.
In fact, it can optimize any evaluable system.
For example, you could use it to determine
the most effective combination of background color,
font size, and link color on a news website's subscription
button.
This would take A/B testing to a whole new level.
Or perhaps you need to find an ideal buffer size and thread
count to minimize computing resources for a job.
Or maybe you need to optimize a cookie recipe, a bread recipe,
or a brownie recipe to produce the most delicious version.
You can, of course, also optimize
nonfood related things, though I'm not sure why you'd want to.
Anyhow, let's talk about how to use the AI Platform
Optimizer to optimize your machine-learning model
or other function.
There are three terms that we need
to understand-- study configurations, studies,
and trials.
A study configuration is the definition
of the optimization problem that you're trying to solve.
It includes the result you would like to optimize
as well as the parameters that affect
that result. Those are basically the inputs that
are being optimized.
A study is the implementation of a study configuration.
A study uses its configuration's goal and input
parameters to conduct experiments or trials.
So then a trial is just a specific set of input values
that produce some measured outcome.
AI Platform Optimizer only suggests
input values for each trial, but it doesn't run your trials
for you.
That is your task.
And a study will continue until it reaches a preset limit
of trials or you end it.
So this means that, as a service,
AI Platform Optimizer suggests trials for you to run,
records the outcomes of those trials,
and then uses machine learning to suggest future trials based
on those outcomes.
And you can continue the cycle for as long as you please.
Now, some of you may recall that AI Platform Training already
has a built-in feature called HyperTune, which takes
care of hyperparameter tuning.
Under the hood, HyperTune uses the same technology
as AI Platform Optimizer.
But whereas HyperTune only works as part of AI Platform
Training, Optimizer is a generic system
that you can use to optimize any system, any place.
This means that your model could be training anywhere--
your local machine, a data center,
or on another GCP service, like Compute Engine or Kubernetes
Engine.
Anything that can make a REST API call
can use AI Platform Optimizer, and that flexibility
is its true power.
Thanks for watching this episode of Cloud AI Adventures.
If you enjoyed it, make sure to like the video
and subscribe to get all the latest episodes right
when they come out.
I'm Yufeng Guo, on Twitter @YufengG.
And if you're looking for longer-form machine learning
and Cloud content, be sure to check out the Adventures
in the Cloud YouTube channel, which I've linked below
in the description.
Ready to give AI Platform Optimizer a whirl?
Head on over to the documentation page
I've linked to in the description below
for all the details.
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