Hello, cloud gurus. I'm Mattias Andersson.
And this is GCP this month for May, 2021.
We have lots of good stuff to tell you about this month,
including the totally not a breakfast cereal,
and still asbestos-free Vertex AI, plus a lot more,
both AI related and not. But before we jump in, I have to ask you,
please answer our survey to tell us what you want to see on our YouTube channel.
I mean, you're already here, right? So you have at least a little time.
Invest it, make your future life better by helping us help you.
Your feedback is seriously valuable. Okay. I'm done begging well for now.
Let's take a look at our quick bites. We'll start with all the non AI stuff.
Security command center premium,
Google's security information and event management or SIEM tool,
now supports folder and project level security in general availability.
So now you can aggregate all of your organization's security findings and assets
in one place,
but still delegate parts of the security oversight while maintaining least
privilege. Nice.
Cloud armor managed protection plus is now generally available.
And it can protect systems from all sorts of external threats,
including DDoSs or distributed denial of service attacks.
This bundles of bunch of services,
including both layer seven and layer three layer,
four protections into an enterprise friendly subscription that also comes with
bill protection.
Blocking function support is now generally available for identity platform.
Identity platform is Google's product to let you manage your customer's
identities and access,
and this new blocking function support lets you set up cloud functions based
hooks and triggers to modify authentication events in real time,
check out the link in the description to see some use cases. Cloud run,
Google's completely serverless and a really quite amazing event oriented
and container-based compute service,
has just gotten support for binary authorization.
This means that you can ensure that only properly vetted and secured containers
will get deployed to handle your incoming events.
Yay for compliance and double yay because it actually improves security
too. Speaking of cloud run security - You could previously retrieve secrets from
secret manager using its API from within your cloud run containers.
But this integration has just been super streamlined in preview.
With just a tad of configuration,
You can now make those secrets available to the container as either an
environment variable or mounted file. Additionally,
you can now protect your cloud run based apps with the identity aware proxy.
This support also in preview can help you move even closer to the beyond Corp
zero trust network model. Now,
if you don't like all the benefits of serverless like scaling right down to
zero, then you can take advantage of cloud run's,
new support for committed use discounts. Okay. Snark aside,
this honestly will benefit some organizations that have a consistent enough
hourly baseline usage of cloud run. So if you are in that situation,
why. Would you say no to this new 17% discount? Right?
Just don't over commit and you're good. Ooh.
It looks like you're on a call helping a customer.
Would you like some help with that? Why?
Yes. Clippy, I mean contact center AI agent assist for chats.
Now that you are in public preview.
I sure would. Thank you. Okay. Jokes aside,
this actually is pretty cool because it gives call center agents a little
virtual assistant, maybe not quite looking like this,
that suggests relevant knowledge base articles based on what the customer is
actually asking and even better.
It also suggests smart replies for the situation based on the nice words
written by your top agents. Sure.
Neither of these things is going to be a hundred percent,
but Google does have data showing that this lets agents handle 28% more
concurrent chats.
And I'm confident that the interaction flow is going to be way better than
Clippy ever was. Sorry, buddy. Okay. This next thing is pretty cool too.
When you need to translate from one language to another,
your text is not always context free. Sure.
If you control the application and your user types it in,
then you're probably good,
but you may often have an entire document like a PDF or Microsoft office file or
something like that.
That's where you would benefit from this new translation API advanced support
for in place, full document translations. Now in preview,
if you have previously been using the cloud monitoring workspaces to organize
your metrics, gathering and reporting across various scopes,
you will be interested to hear that Google has now done away with those in favor
of metrics scopes.
Now you don't really need to do anything for this change.
As Google says that all of your existing cloud monitoring workspaces have been
migrated to the new data model, but it's helpful to be aware of the change.
So you're not taken off guard. Okay. Now let's dig into our GCP. Yeah.
Remember how last month I told you about AI platform unified becoming generally
available. Ha, well this month AI platform.
Unified no longer exists.
And Google has pretty much scrubbed it from all their documentation, but Hey,
don't worry. There is no coverup conspiracy here. Oh, uh,
I guess I don't need this.
It's actually still alive and well just christened with a new name.
Vertex AI.
And the ceremony for that christening was this month's Not normally So cloud
related Google IO conference.
I really think the Google cloud team was having conference withdrawal or
something.
Given last year's 14 week cloud next on air And this year's cloud next not
happening until October right? Now,
Google has just put out an absolute ton of new blog posts and videos,
this new Vertex AI. And I do get why,
because it's actually pretty awesome.
That said I would not blame you one little bit for feeling like it's quite hard
to tell what's really new this month because everything under this umbrella has
also been renamed to the new Vertex, something or other,
but no matter the name,
this is a very powerful and practical suite of products to enable
you to take advantage of machine learning no matter who you are or what your
background in particular Vertex.
AI is all really about streamlining people's adoption and
operationalization of machine learning.
I turned the keeps coming up is ML ops,
the practice of applying dev ops and SRE approaches to developing and
operating machine learning models.
So let's think about people's real experiences.
People's first introduction to ML is often with a simple API fronting,
a pre-trained model, just call this one thing from your code.
And it'll give you an answer,
whether that's about your image or your text or something like that.
The only data you have involved is what you send in for the prediction.
But this is just the tip of the value iceberg,
because things get really interesting when you have your own historical data set
and that's where Vertex AI starts coming in. First off Vertex,
AIS managed datasets makes it much easier to wrangle your data, ingest,
analyze, transform, and label it.
Then when it comes to making and training a model,
you can either use the simpler auto ML tools, which are great for images, video,
text, or tabular data. But if you want or need more control,
you can then use Vertex training for custom models using frameworks like
TensorFlow or Py torch.
And you can use Vertex Vizier to tune your hyper parameters.
Then once you've got a trained model,
Vertex explainable AI helps you evaluate it.
And Vertex prediction handles deployment very nicely too,
with consistency between custom and auto ML plus support for multiple endpoints
and traffic splitting that traffic splitting comes into play because this is all
oriented around ML ops, like I mentioned.
So you'll use Vertex pipelines to do all of this stuff automatically and
continuously and Vertex,
tensor board to visualize and analyze experiments to improve your models and
model monitoring to continuously watch your models performance over time.
Then Vertex feature store can let you serve,
share and use ML features across your organization.
It seems like a lot, but wait, there's more.
But seriously, I need to cut it off here. If you still want to dig deeper,
I've got links for you.
The best video I've found for this is the very efficient Google IO session by
Sarah Robinson. Of course I'll include a link to that in the video description.
Unfortunately,
this particular video of hers doesn't include any of her famous ML based baking
adventures, but I'll include a link to one of those too, just because I can.
Another valuable video about Vertex AI is by Priyanka Vergadia.
Also quite efficient,
but just taking a slightly different approach and leave us a comment on this
video. If you find some other good resources sharing is caring. All right.
Now I know that at the beginning, I asked you to answer our survey,
but assuming that you didn't immediately pause and go and do that,
then please don't forget to do that now. Oh, oh yeah.
And also do the likey subscribing thing, but the survey is even more important.
Well, that's what I've got for you this month.
Tune back in next month for more exciting GCP news,
stay safe and keeping awesome cloud gurus.