SUDS NARASIMHAN: Hello, my name is Suds Narasimhan.
I'm a product manager in Google Cloud for Cloud AI Notebooks.
Today, I'm going to walk you through an overview of Cloud AI
Notebooks our managed JupyterLab notebook
service for enterprises on Google Cloud.
Today, we'll explore how Cloud AI Notebooks can
help you explore data quickly and develop an AI-enabled model
and deploy it into production.
Artificial intelligence and machine learning
are one of the most--
are one of the key disruptive technologies
that enterprises have encountered in the last four
or five years.
And AI and ML will continue to disrupt enterprises going
forward for the next 10 years.
The key to unlocking value with artificial intelligence
starts with a model.
And the simplest way to develop a model is to use notebooks
and, within notebooks, to use open-source frameworks
to develop AI models.
TensorFlow, PyTorch, and many other OSS distributions
exist today that can help you quickly develop a model
and gain insights.
As, a data point, today, more than 8 million Jupyter
notebooks run on GitHub.
And this notebook count only is going to continue to grow.
Google has been in the forefront of bringing notebooks
to different segments of customers over the past decade.
Kaggle, for example, is targeted at enthusiasts and people
who are learning to be data scientists.
Colab is targeted at individual researchers and students.
And it provides a collaborative environment
for developing data science algorithms.
AI Platform Notebooks are targeted towards enterprises.
This means that AI Platform Notebooks
come with security built in, which enterprises really
care about.
Today, we're going to spend some time looking at AI Platforms
and how it can speed up your model development.
So, as we noted, enterprises want
insights in days, not weeks.
This means that data scientists want
to focus on the difficult job of model development
and not, essentially, the peripheral task that
goes around running a notebook or the infrastructure
associated with a notebook.
Enterprises encounter several challenges
in running notebooks and open-source frameworks.
The first thing is around managing open-source frameworks
and making these--
making the operating systems, the frameworks,
and the dependencies work correctly.
Hosting notebooks and maintaining them
on top of these frameworks is an additional pain point.
Customers also want to make sure that the access
to the data that data scientists use
and the models developed from the notebook themselves
are secure, and they're only accessible
by the right personnel.
Since AI and ML involves accessing data
across the enterprise landscape, it
is important for a notebook service
to connect with all of the data sources that
reside within an enterprise's data estate.
Our AI Platform Notebook service solves these problems
by providing three key features.
We completely manage the installation
of the Jupyter notebook environment,
along with all the open-source frameworks and dependencies.
So you don't have to manage any of that lifecycle.
Our notebooks also ship with security features
like VPC-SC, CMEK, and other GCP horizontal features built in.
And Cloud AI Notebooks is also integrated with several data
services like BigQuery and Dataproc
that we'll dive into a little later.
Notebooks are really easy to get started.
All you have to do is go to the GCP console,
find Notebooks under the AI Platform Notebook,
create a new instance with the required
deep-learning framework and the type of VM
that you want to run the notebook on.
So what makes Cloud AI Notebooks a fully managed experience?
What we do, as part of Cloud AI Notebooks,
is to pre-install all the client libraries and all
the environments.
We support popular open-source environments
like TensorFlow, PyTorch, and mathematical libraries
like NumPy and SciPy.
To plot, we also provide open-source tools
like Matplotlib built right into our deep-learning VM.
On top of these deep-learning VMs,
our notebooks run, and they take advantage
of these deep-learning libraries.
Customers can configure the notebooks
through the Cloud console.
However, they can also get a link
to access the Jupyter notebook using a proxy outside
of the GCP console.
Cloud AI Notebooks are designed to work
with different kinds of compute instances within GCP.
Cloud AI Notebooks can run with DLVMs, as we just spoke about.
These are GCE instances that come pre-installed
with deep-learning frameworks.
Cloud AI Notebooks can also work with Dataproc clusters
and Dataflow.
Cloud AI Platforms also work with custom containers.
So, if you have a specific deep-learning library
that you want to run within the custom container,
you can do so and install a notebook
on top of these containers.
We will talk a little bit more about our Dataproc integration
in the slides that follow.
Cloud AI Platform also works with data sources within a GCP
like BigQuery.
So you'll be able to query data that's stored within BigQuery
and use that data to train your models.
Let's take a look at how AI Platform Notebooks
are architected.
AI Platform Notebooks are designed
to work with a variety of compute instances.
We just talked about how it works with deep-learning VMs,
which are GCE container--
GCE instances with pre-installed deep-learning
frameworks within the VM.
AI Platform Notebooks also work with Dataproc clusters
and Dataflow.
AI Platform Notebooks also work with custom containers,
which means that, if you have a special deep-learning framework
that you want to run, you can create a custom container out
of it and have AI Platform Notebooks work
with those containers.
AI Platform Notebooks work with a variety of data sources
within GCP.
For example, you can query data from BigQuery,
use that data as the basis to train your model.
AI Platform Notebooks are also well
integrated with Git repositories and other source control
systems.
So it's easy to push and pull code into these repositories.
So why would you want to use AI Platform Notebooks?
AI Platform Notebooks makes it really easy
to get started with your model development.
Since you don't have to deal with installing
these deep-learning frameworks or setting up the notebook
environment, you can get started very, very quickly.
In addition, since AI Platform Notebooks
is a managed JupyterLab service, the environment
around code development is very familiar for data scientists.
AI Platform Notebooks enable you to scale and control costs
effectively.
So you could start with a very small instance
for model development.
And, as you ramp up your training,
you could move to a bigger VM.
Or, better yet, you could use a GPU or much powerful machines
in order to train your models.
AI Platform Notebooks comes with security built in.
So, as we talked about VPC-SC, IAM controls, as well as
CMEK controls are built right into AI Platform Notebooks.
AI Platform Notebooks are also integrated with other services
within GCP.
So it plays within an ecosystem of analytic tools,
as well as our AI Platform.
So AI Platform makes it really easy
to build, train, and deploy these models.
Let's talk about some recent updates
that we have made to our notebook service
in the last six months.
We recently announced an integration with Kaggle.
Kaggle, as you may know, is a community
of 5 million data scientists who write and share code.
With this integration between Kaggle and AI Platform
Notebooks, customers have access to GCP's
limitless and customizable compute environment
that allows users to scale up their work very easily.
We've already seen thousands of customers from Kaggle
go to Cloud AI Notebooks using this integration,
and we expect these numbers to grow in the next few months.
One of the recent integrations we announced
was with Dataproc through the smart analytics framework.
The smart analytics framework brings together
Apache Beam and Dataproc together
with AI Platform Notebooks.
Customers typically need to analyze petabytes of data
before starting to work on a model.
With this built-in integration with Spark,
analytics and deep learning can be authored from one place.
The Dataproc Hub brings enterprise controls
to Spark clusters based on notebook interfaces.
If you want to learn further about Apache Beam notebooks,
there's a fantastic tutorial when you launch--
when you launch a notebook using smart analytics framework
and when you choose Apache Beam.
As you can see, the experience to using
smart analytics framework is super easy to get started.
All you would have to do is pick the smart analytics frameworks
choice and either pick Apache Beam or Dataproc distributions,
instead of a TensorFlow or a PyTorch distribution,
as you would for a deep-learning environment.
So the goal of Cloud AI Notebooks
is to help you develop models quickly and allow you to easily
scale and streamline that model within your production
workflows.
Let's take a quick look at some of the examples
around how you can go about doing that.
Cloud AI Notebooks provides a means to plug into your data
pipelines that enables feature engineering
as well as data labeling very easily.
Cloud AI Notebooks helps you model your data efficiently
with what-if analysis and, also, embedded AutoML capabilities.
You can evaluate your model quickly
with feature attributions, what-if analysis,
and continuous evaluations.
We've also built in algorithms like optimization, AutoML,
and NAS right into Cloud AI Notebooks
that makes model developments simple and faster.
On top of this, Cloud AI Notebooks
runs on a hybrid platform and a multi-cloud platform that
also has access to some of the most powerful compute you
will find anywhere in the world, for example, TPUs.
Cloud AI Notebooks makes it easy for you to scale and streamline
your model development with ease.
Cloud AI Notebooks works in an ecosystem of data services
and AI services.
Cloud AI Notebooks can be easily integrated
into your data pipeline.
So, if you ingest data using Data Fusion
or transform data using Dataproc and put it into BigQuery,
you can easily access that data, label the data,
and use that data for training.
As previously mentioned, Notebooks also
makes it easy to build, develop, and train
a model with built-in tools such as prebuilt algorithms, AutoML,
and access to our training service.
Cloud AI Notebooks also makes it easy to deploy these models
by verifying your model performance using
explainable AI, as well as directly deploying the model
into our prediction service.
Our prediction service also ships
with a continuous evaluation feature
that helps you to monitor the model once it's in production.
All these components are built with access
to a pipeline that makes it easier
to containerize and monitor the entire workflow end to end.
One such tool that's built into AI Platform Notebooks
is Explainable AI.
Explainable AI is a set of libraries
that help you figure out why your model made
a particular prediction.
For example, on the right, you can see a color-coded heat map
of what the most important features were in making
a particular prediction.
Explainable AI can be useful tabular, image, text TensorFlow
models.
Explainable AI also ships with a built-in What-If Tool.
[INAUDIBLE] What-If Tool, you could change the values
for attributes that you're building with a model
and figure out how much it impacts the prediction
of that particular model.
Explainable AI libraries are already
available in some of our deep-learning distributions
today.
And they will be available in more in the coming weeks.
Of course, AI Platform Notebooks runs
in an AI-optimized infrastructure within GCP.
For example, our v3 TPU pods provide 84% faster performance
in both object detection and machine translation
compared to on-prem infrastructure.
This AI-optimized infrastructure is
only available on GCP among public clouds.
And they clearly outperform on-prem systems in MLPerf.
Looking ahead at our roadmap extending beyond 2020,
it's worth looking back on where we came from.
We launched AI Platform Notebooks in beta
in April 2019.
And, since the time that we have launched,
we've seen tremendous interest among enterprises
for developing models using Cloud AI Notebooks.
We've seen significant user growth and adoption
in the last year that helped us successfully
GA the product in March 2020.
Beyond 2020, we hope to build in features
around machine learning operations, lineage tracking,
collaboration, and integration across our entire analytics
estate.
As next steps, we invite you to read our Notebooks Manifesto
blog that's available on the Google Cloud blog site,
as well as get started with Cloud AI Notebooks using
a five-minute tutorial that's available on our product page.
You can also learn about all the features
in in-depth documentation on the product page on Google Cloud
Platform.
Thank you for joining us today.
I hope you enjoyed this presentation
and you have a good rest of the day
and you enjoy our other Next '20 presentations.