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SARA ROBINSON: What if Google Cloud Platform's machine
learning tools were all part of one pyramid?
Literally, as I was trying to fall asleep, I had an idea.
I'm Sara Robinson, and I'd like to help you figure out
which ML Tool is right for the problem you're trying to solve.
Google has a bunch of different ML products
to get you from point A to point B--
in order to provide flexibility in choosing which path
you want to take, based on your cost effort
and speed requirements.
So I wrote it all out really quickly.
Then, I edited it the next day and sent it out.
I guess it resonated.
The idea is, as you move up the pyramid,
less ML expertise is required.
And you also don't need to worry as much
about the infrastructure behind your model.
The pyramid is organized by levels of abstraction.
Let's start at the bottom.
At the lowest level of abstraction,
we have these popular open source
machine learning frameworks.
If you're building a model from scratch using these frameworks,
you get the flexibility of moving your workloads
across different development and deployment environments.
As a result, you need to manage all the infrastructure
yourself, for training and serving.
If your team has very specific needs,
and you have the ML engineers and data scientists,
you might prefer the total flexibility
of using these open source ML frameworks on their own
and figuring out where to train and serve them.
Moving up one tier, Deep Learning Virtual Machines
provide managed click-to-deploy VMs
for processing data and training your model.
These VMs come with popular ML frameworks
pre-installed, but leave you to figure out exactly how you'll
serve those models.
For instance, you could serve them on-premise
or directly on a mobile device.
With Deep Learning VMs, you can reduce the overhead
of managing and allocating the compute and storage resources
required for ML workloads.
Kubeflow is an open source project
created by Google for deploying your ML
workloads on Kubernetes.
You use it to configure a multi-step ML pipeline,
with different components for pre-processing data,
training, and serving your model.
Because it's open source, you can run it
on-premise or on AnyCloud.
The other way of saying that is, what you'll still need to do
is configure where it's managed.
With Kubeflow, you can leverage the power of containers
to make your ML stack portable, reducing
the operational overhead of running your workloads.
AI Platform takes this "where" question out of the equation.
It's all handled on Google Cloud Platform, our managed
service for all your custom model needs.
AI Platform is entirely server-less,
so you can focus on your specific workloads and business
logic.
The platform includes tools for training and serving
models, post to Notebooks, a data-labeling service,
and more.
For example, you could take the same Notebook
code you're running on-premise with Kubeflow,
and run it on GCP with AI Platform Notebooks.
And something else I should mention,
you don't have to use AI Platform for both training
and serving.
If you wanted, you could let AI Platform handle one, and figure
out the other on your own.
For example, you could take advantage of AI Platform's TPUs
to train your model, and then serve it on-device.
By the time we get to BigQuery Machine Learning,
you no longer need a team of ML engineers and data scientists.
But it might still be helpful to have a few.
Unlike the products below it, with BQML,
you don't have to write any of the underlying model code.
You'll still need to choose which model
type you want to use, when you run your create model query.
Running this query will create and train your model.
And once you run it, your model will be automatically deployed
for you, and available to make predictions
with a single SQL query.
With BQML, you can bring the power of ML
closer to where the data is analyzed
and make it accessible to data analysts.
But what if you didn't need to write model code
or choose your model type?
That's where AutoML comes in.
All you'll need to do is gather and upload your data,
whether it's images, video, text, or structured data.
Then, once you press the Train button and training completes,
your model is immediately available for prediction
via an API.
You don't need to deploy it yourself.
You can also use AutoML's UI to evaluate your model quality
and update training data.
AutoML democratizes machine learning
by empowering more people to build custom
ML models that solve a wide variety of business problems,
regardless of ML expertise.
For both AutoML and the tip of the pyramid, APIs,
you don't need any ML engineers or data scientists
on your team--
just a handful of developers.
What makes our APIs different from AutoML,
though, is that you don't need to supply any training data
yourself.
Just make an API Request, and you'll
get access to pre-trained models for images, video, speech,
text, and translation.
Our ML APIs are the easiest and fastest way to get
started with AI today.
I'm Sara Robinson.
And hopefully you're not losing any sleep
over choosing the machine learning tool that's
going to help you the most.
Leave that part to me.
Be sure to check out the GCP YouTube channel for more
relevant informative content.
And if you found this helpful, don't be
afraid to like and subscribe.
Thanks for watching.
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