Melinda Marks: Hi, everyone, we just got back from
Google Cloud Next. And we want to share some
highlights. I'm here with Mike Leone, my
colleague. So Mike, the theme was Gen AI. And
you've been covering that for a while. What were
the key highlights and takeaways that you saw at
the show?
Mike Leone: Yeah, what an event like Google
continues to cement themselves as the leader in
this space. They're innovating so rapidly across
really the entire portfolio. So it really
highlights a shift that we're seeing where look,
organizations want innovation, they want value
from these AI and Gen AI investments. And Google
Cloud is proving to be the partner to give it to
them. And look, there were so many announcements.
A big focus was around vertex, that's their
enterprise AI platform, they really continue to
evolve this platform. I like the positioning, how
it's really being positioned as an open platform.
It sits on top of the Google Cloud infrastructure
that's increasingly being powered by Google's
TPUs. There was an announcement around the latest
generation of GPUs and the performance, cost
optimizations, power efficiency, etc. I want to
add on a few of the other announcements that I
think really warrant some focus specifically in
model garden, Gemini 1.5. Pro now it's public
preview, up to a million token context window that
is massive. I believe that's the largest context
window in the industry to put it in perspective,
right, that supports 700,000 words 30,000 lines of
code. And again, it's multimodal, right, so that's
11 hours of audio, one hour of video, just massive
scale. Something else imagine 2.0 may highlighted
live image support watermarking on the anthropic
side, I guess mentioned this, but now, haiku and
sonnet are available in model garden, their Opus
is coming soon, the reason that that's a big deal,
I don't know if you pay attention to some of the
benchmarking that goes on in this space. But those
models perform amazingly right now. They're
leading the market and several if not all areas.
The other piece I want to hit on super important
enterprise readiness, I really, really liked the
focus on enterprise readiness, one of the big
components they're grounding with Google search,
obviously, there's been a big focus on reducing
hallucinations improving trust and grounding is
that avenue to get there. So there were some
announcements related to bringing in enterprise
data through RAG. But this grounding with Google
Search really lets organizations use public data,
use Google Search to ground and help, you know,
cite a response, where it came from, why it came
from there, etc. The other area that stuck out to
me, associated with trust and grounding is the
expansion of their ml ops services. So prompt
management side by side comparisons of responses
through different models explainability tied to
those comparisons that can highlight why one
response outperforms another with certainty scores
and accuracy, percentages, all that kind of stuff.
So some really differentiable features there from
Google Cloud, which I liked. And I mean, look,
I've already highlighted a bunch of you can tell
I'm excited about these things. But I want to turn
it back over to you, Melinda, because security is
such an important component to everything going on
in this AI engine AI movement. What were some of
the takeaways on the security front from you?
Yeah,
Melinda Marks: you're exactly right. And I really
love this theme, where they're talking about and
what is enterprise grade. So they throw out all
these improvements, all of these things that
they're enabling developers to do to build and all
of these amazing tools with high performance, but
you have to make sure that the only way that you
can get to enterprise grade is you need security,
stability, resiliency. And if there are any
security issues is the point that I've been
making, especially across all the different
hyperscalers shows is if security teams can't
handle this, they can't support it, then sometimes
you have to go backwards and you can't do your
digital transformation, you can't support that. So
security has to be very important. And you have to
make sure that you can scale and support that
growth to support that technology adoption, that's
going to give you that competitive advantage. And
we've seen so much about Gen AI is so poised to be
game changing technology. So we want to make sure
that security teams are poised to know what they
need to do to set the right policies understand
where it's used. And all those things are areas
where Google's trying to help customers. And it's
really nice to see that they're doing a lot in
these areas. So they have things like their
unified data foundation with Big Query proving
data management. And this is really what Google is
known for is the search capability of
capabilities. That is going to be really a helpful
Foundation to help with security because you need
the right policies, the right governance, the
right visibility and control to make sure that you
can secure it. So in terms of announcement they
actually talked about how they're applying their
Gen AI to supercharge security operations. And so
applying Gemini to enable natural language
searches for investigation, and this is something
we've been addressing a lot in our research on
Cloud detection and response, how difficult it is
to detect and respond quickly. So being able to
add that there's a lot of other CSPs, who have
talked about the their Gen AI improvements, but
applying it to security operations is, I think, at
Google, they're making a really good example of
how to do that, where you're really helping, and
they could demonstrate this in their demos of how
they're using things like Mandiant threat
intelligence repository. They're using search and
AI to help suit security analysts processing
information for rapid detection and response. The
other interesting announcement that came out was
that their their Chrome enterprise premium offer.
So we've seen some startups in the browser
security space, we know that employees are
frequently working out of their browsers. And
Chrome is a leading browser. And, you know, even
if you're a Microsoft Office user, a lot of people
work out of Chrome. So I see this as a great way
for them to get more customers and expand their
usage into Google Cloud Platform. And adding those
security capabilities enterprise features to help
with issues including things like malware and
phishing that are harder for enterprises to it, we
see so many vendor tools for security, but there's
so many fundamentals that are still difficult
things like social engineering, malware problems,
those are things where Google can help because
they have all that data. They have the Gen AI,
where they can help aid humans to make sure that
they avoid these malicious beings who are trying
to attack them. So Google can really continue to
expand its user base. And it's really nice to see
them finding ways to expand. And I thought it was
really interesting too Mike in the q&a session
with Thomas Kurian. He said that Google had three
key differentiators spurring organizations to make
this move and this migration. So cybersecurity was
one of them. And then they also said data
analytics and AI were the other two. So I just
talked about the advantages for cybersecurity.
What do you think, on him saying that data
analytics and AI are there two strong
differentiators?
Mike Leone: It's funny, I didn't even mention some
of the great analytics announcements that they
made early on, right. So you know, Gemini
availability within BigQuery, and Looker, really
taking data interaction to the next level, they
can really combine structured and unstructured
data in a single system, and then enable customers
to access that data no matter where it is. So that
scalability and convergence is really the
differentiator for them on the data side. And then
if you add in some of the streaming announcements
from the event, like BigQuery, continuous queries
and Apache Kafka for BigQuery, the ability to tune
and ground multimodal LLMs using BigQuery data,
you know, the world is really recognizing the
power of what a converged platform like BigQuery
can bring to their business. So it's really
scalability and convergence there of trusted data
foundation. Now, from an AI standpoint, they have
that open enterprise grade AI platform and vertex
and they're continuing to evolve that story. It's,
it's been pretty impressive, honestly, to see the
changes they've already made within that platform
over the last 12 months or so based on feedback
and how quickly the markets moving. And of course,
I mentioned some of the differentiation too, they
have just from a feature and functionality
standpoint, write the announcements around side by
side comparisons, they're doing some unique things
with synthetic data generation and, and
distillation. It's really about empowering orgs to
run right size models anywhere. So again, stay
tuned for more from me, Melinda, any last thoughts
here?
Melinda Marks: Yeah, just that that ease of use
for people to use Gen AI. And then when you think
of security having to not work as hard because of
all those built in features. And again, with Gen
AI. To me, it's a lot of stuff that people are
already having challenges in things like tagging
data, being able to find it, a lot of people don't
know where their sensitive data is. It's all that
stuff is built in better and makes it that much
easier to adopt it while enabling security to do
the things that they need to do, which is set
those guardrails. So Mike, thanks for being here
with me today to give folks a quick idea. This we
barely scratched the surface. So for those who
watch thanks for joining us, and be sure to follow
us and we'll be blogging and sharing a lot more
information coming soon. Thanks for joining and
we'll see you again soon.