ALEXIS MOUSSINE-POUCHKINE: Can you
get a sense of Google Cloud Platform
machine-learning products in just a few minutes?
Let's find out.
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Despite all the attention lately, machine learning still
remains somewhat difficult to build, maintain, and deploy.
Thankfully for you, Google Cloud Platform
puts a lot of that behind the scenes,
so you don't have to worry about it.
So what does the [? hello ?] world of ML
look like when you don't have to write the neural nets yourself?
Simply calling the Vision API to describe
what it finds in the picture.
As a leader in machine learning, Google Cloud
offers several high-quality pre-trained models via APIs
that can easily be consumed by developers regardless
of the language they use or the nature of their application.
Developers can invoke the API directly or using
one of the idiomatic client libraries
available in numerous programming languages.
Here, we invoke the API directly from the product description
page.
From this image, the API can detect a broad set of labels,
provide dominant colors, and other image properties.
It can also extract text, identify entities,
such as landmarks, celebrities, logos, or news events
along millions of entities that it supports.
It can also moderate content such as all of your app's
user-generated content.
In addition to the Vision API, GCP
offers pre-trained models for text
to speech, speech to text, natural language, translate,
all via easy-to-use APIs.
Now, pre-trained models are amazingly easy to use,
as we've seen, but they're not trained on your setup data.
This is where a Cloud AutoML comes in.
Cloud AutoML is here to enable developers with limited machine
learning expertise to train high-quality models specific
to their business needs by leveraging
Google's state-of-the-art transfer learning and neural
architecture search technology.
Cloud AutoML provides a simple graphical user interface
for you to train, evaluate, improve, and deploy models
based on your own data.
You're only a few minutes away from your own custom
machine-learning model.
Now, the ML APIs and Cloud AutoML
are great when you want to use or build
upon a pre-trained model that Google already has training
for.
But what if your needs are beyond that?
Well, that basically requires training your own models
with your own data sets on your own virtual machines.
And I don't know about you, but I don't always
have time for that, which is why I use Cloud Deep Learning VM
Image.
These Google Compute Engine instances
come pre-installed with the latest versions of TensorFlow,
PyTorch, and scikit-learn.
You can also add cloud CPU and GPU
support with a single click.
This image also comes with the latest software drivers
and third-party dependencies, including the latest GPU
software, and is available using the GCP
Marketplace from the console or using the command line.
If you'd like to get the operations out of the way
and focus on your models, consider
using Cloud ML Engine which offers fully managed training
and prediction services for developers and data scientists.
You can build your models with multiple ML frameworks
including scikit-learn, XGBoost, Keras, and, of course,
TensorFlow.
Cloud ML Engine trains any model at large scale
on a managed cluster.
HyperTune is a unique feature that
enables you to reach better results faster
by automatically tuning deep-learning hyperparameters.
Cloud ML Engine then offers online predictions, adjusting
to any requests rate coming from your ML-enabled applications
and does so through a secure web endpoint.
Batch predictions are also supported
for really cost-effective inference
for asynchronous apps.
Whether you're new to machine learning
or you're already an expert, GCP has a variety
of tools to help you, from pre-trained ML
models accessible via simple API call,
to customizing models with AutoML,
to training and serving a custom TensorFlow model using Compute
Engine or Cloud ML Engine.
Consider taking free codelabs, linked in the description
below, to explore these products.
Check out the compute and big data overview episodes.
And look forward to an upcoming video on big data storage
and processing.
Please like, subscribe, comment, and share.
See you soon for another GCP Essentials video.
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