STEPHANIE WONG: Hey, everyone.
We've got a special episode of "Take5"
today, where we're going to answer the top 10 questions we
hear from customers.
YURI GRINSHTEYN: Wouldn't that make it a "Take10?"
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STEPHANIE WONG: And our first question is--
what exactly is a customer engineer?
YURI GRINSHTEYN: Great question.
In a sentence, customer engineers
exist to help our customers transform, evolve,
and yes, engineer their business applications on Google Cloud.
We do that by meeting with customers
one-on-one to understand their current state
and where they'd like to be in the future.
We then work with customers to navigate the best path
to get them from today to tomorrow,
help identify the right technical solution within GCP,
assemble a proof of concept, collaborate
with them with a proper architecture,
and design a migration plan to safely and securely migrate
their data to its destination.
Our ultimate goal is to help customers be successful
in Google Cloud platform.
And we really own that end-to-end technical
relationship between Google Cloud and you.
STEPHANIE WONG: That's how we roll.
Question number 2-- if I want to run my application on GCP,
how do I choose which product to use?
NICK ORLOVE: Hmm.
That's a tough one because, honestly, it depends.
I know that seems like a cop-out answer.
But to recommend where you should
be running your application, it's
best to fully understand your application requirements.
GCP offers four primary means of application hosting--
those being Compute Engine, Kubernetes Engine, App Engine,
and Cloud Functions.
Google Compute Engine, or GCE for short,
is our infrastructure as a service offering, which
allows you to create fully managed
and customizable VMs for running your apps.
If you're interested in running a containerized application,
Google Kubernetes Engine, or GKE,
might be the best option, because GKE
Is our fully managed and hard in container orchestration
and management service.
If managing the infrastructure isn't your jam,
check out App Engine, which is Google's platform as a service
solution, and lets developers completely
focus on building great applications
without the management overhead of dealing with servers
or configuration deployments.
Finally, Cloud Functions is Google's
event-driven and serverless platform
that only runs your code in response
to event-driven triggers.
The best news is you can also pick a combination of computing
offerings.
So you don't have to just rely on one.
YURI GRINSHTEYN: And I got one.
Now that we know about application hosting,
my app obviously needs storage.
Question 3-- how do I decide where to store my data.
STEPHANIE WONG: Well, there are many fully
managed offerings here for that data that's
either structured, unstructured, transactional, or relational.
Persistent Disk offers block storage
that's suitable for VMs and any kind of containers,
like Google Compute Engine or Kubernetes Engine,
as you mentioned.
Cloud storage is used for object storage
and can store streaming multimedia,
or custom data analytics pipelines, or even backups,
and disaster recovery.
And then Cloud Bigtable is a NoSQL wide-column database
that's suitable for real time access and any kind
of analytics workloads that need low latency and high throughput
read, write access.
Cloud Data Store is a NoSQL document database
for Web and mobile applications.
And Cloud SQL is a manage MySQL and Postgres database service.
Cloud Spanner is a mission critical relational database
service with transactional consistency and global scale.
And cloud BigQuery is a fully managed Enterprise Data
warehouse with SQL syntax and real time
response for ad hoc querying.
Lastly, we have a number of databases and storage
used for Firebase, our mobile and web app hosting platform.
NICK ORLOVE: Oh, hey, a penny on the floor.
[WHOOSH]
IVAN RAMIREZ: I think we took a wrong turn.
Oh well, onward with question number 4.
Everyone is talking about machine learning these days.
But I just don't know where and how to get started.
What's the best way to get started with ML if not a data
scientist or robot?
CORI PEELE: Well, Google wants to democratize AI and make
it accessible and useful for enterprises and developers.
We offer ready-to-use models so you
can focus on the application and not the science.
Using our machine learning REST APIs is quite simple.
Google Cloud provides fully trained machine
learning models for vision, video intelligence, speech,
translation, natural language processing.
And these are extremely easy to use if you are
a developer familiar with REST.
So the ML APIs are pre-trained on Google datasets
and ready to go.
Anyone that knows how to call a REST API
can integrate with them from their applications.
If you want to train your own datasets,
instead of relying on Google's data,
then Cloud AutoML allows you to create
models specific to your domain.
No programming experience or machine learning experience
is required.
You simply identify your training data and labels,
and Cloud AutoML does the rest.
Lest we have the world's most popular ML
framework, TensorFlow--
and this is available as a managed service
called Cloud ML.
This is for those with theoretical machine learning
knowledge and those who are comfortable writing code.
Check out the blog post, "Learn TensorFlow
and deep learning without a PhD" if you're
a developer interested in this option.
IVAN RAMIREZ: It sounds like Google has a lot of experience
with big data and machine learning.
Question 5-- if my business is running on GCP,
does Google analyze the data I've stored in the Cloud?
MARK MIRCHANDANI: Not at all.
Many people are concerned about their data.
And privacy is a big deal for Google,
as well as every other company.
Our focus is on making sure only you have access to your data.
Not only does Google not read or analyze your data,
we also encrypt everything and give you the ability
to use your own encryption keys to make sure you're
the only one who can read it.
This applies to all of the Google Cloud Services.
IVAN RAMIREZ: Thanks everyone for watching.
Join us in the next episode with the remaining top 5 questions.
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