PRIYANKA VERGADIA: Hello, and welcome to "AI,
the Future of Us," a podcast-style video series that
explores the ways in which AI is shaping our future
and how we can prepare for the changes
that lie ahead, as developers, as people in cloud,
cloud architects.
I'm your host, Priyanka Vergadia.
And today, we are joined by Janakiram MSV, who
is an analyst in the cloud industry
and space for quite some time.
Thank you so much for being here, Jan.
JANAKIRAM MSV: Thanks, Priyanka.
It's my pleasure.
PRIYANKA VERGADIA: Amazing.
So let's kick this conversation off
with a little bit of background and your expertise in cloud
and in AI.
JANAKIRAM MSV: Sure.
So I spent a lot of time at Microsoft,
about a decade, followed by a couple of years at AWS.
And for the last decade or so, I have been a one-man shop,
primarily focused on market research analysis
and the evolution of cloud.
PRIYANKA VERGADIA: And how is AI part of this?
What have you been doing so far in AI
and learning about, based on this research?
And where do you see this going?
JANAKIRAM MSV: Right.
So when AI was just picking up the momentum a few years ago,
I think in 2014, 2015, I realized
cloud is going to be the powerhouse that
will generate the electricity.
Sorry, Andrew Ng, but if you call AI as electricity,
I call cloud as the powerhouse.
So I realized that is going to be the logical home where
a lot of AI models are going to be generated and trained.
So I started focusing on that.
So I started my journey by understanding
what is machine learning and how is
it different from traditional software development.
So initially I used tools like sklearn,
which a scikit-learn, and used managed cloud services,
like Google Cloud ML Engine, Azure ML, and very
early versions of SageMaker.
I learned the basics and the fundamentals of ML
by just looking at how this is becoming a managed service
offered by the hyperscalers.
So it started from there, and today it
has come to a point where a lot of complex AI-based services
are offered as APIs.
You don't need to know a lot of AI.
Thanks to the evolution of foundation models
and generative AI, it is just becoming just an API
to consume.
But this API will empower and enable so many developers
and organizations to infuse the power of AI
into their applications.
PRIYANKA VERGADIA: Yeah.
So it's like you talked about electricity and the powerhouse
and powering that.
And that's kind of where, basically, cloud
lies, where cloud is going to be this powerhouse of igniting all
of these AI APIs so that not everybody has
to be an expert in AI and ML, but everybody
can utilize AI and ML, so sort of like democratizing
the power of AI and ML.
With that, where do you see as effective uses of AI
in the cloud industry or just in tech industry in general?
JANAKIRAM MSV: Right.
So AI is going to impact and influence
almost every vertical, from entry-level jobs,
like data entry or converting Excel to PDF,
or just digitizing.
From there all the way to very sophisticated, very advanced
SRE jobs, AI is going to influence
almost every vertical and every individual in this industry.
Now, since I focus on mode of operations
and specifically cloud operations and DevOps,
I'm seeing some interesting things
that are going to come up.
Number one, the whole application of LLM
signed GPT-like models for operations
is going to be revolutionary.
Now, just large language models are trained on textual data,
and they're able to autocomplete and generate content.
Imagine training these LLMs with logs
that are generated by open source software containers.
And these logs are going to be pretty standard, whether you
run it in minikube or in a multi-node clustering GKE,
the logs are going to be the same,
the error messages are going to be the same.
So imagine training LLMs based on the observability data
coming from infrastructure workloads
and the supporting open source software
and then creating an automation, an agent-driven automation that
can not only suggest what needs to be done,
but can even remediate.
So that takes over your escalation, basically
the support escalation of L1, L2, L3.
And LLMs will play a very powerful role
in analyzing the logs that come out,
segregating them, which is basically classification,
and then trying to remediate.
And only if they fail to remediate,
it gets escalated to an SRE, who is responsible for the uptime.
So this is going to impact the way we do DevOps today,
all the way from writing code to deploying it in production,
and even [INAUDIBLE] operations.
So this is going to be huge and significant.
It is going to impact the DevOps vertical and the overall Cloud
operations vertical significantly.
PRIYANKA VERGADIA: That is such a great example.
It's also, like, end-to-end use of AI in the entire lifecycle.
What are some of the-- because this also points to the skills
that we would need to, as individuals in this DevOps
ecosystem, would need to either possess or grow into?
So what are some of the key skills
that individuals should possess to be successful
in this AI-driven world, maybe with that example
or any other example?
JANAKIRAM MSV: Right.
Just like anything else, we are going
to see two personas in this ecosystem.
One persona is responsible for fine-tuning these language
models for infrastructure.
They are very few.
They understand how parameters work, what is fine-tuning,
and how do we apply custom datasets.
So that is going to be one persona.
And that's a very, very unique skill and a special skill
to have.
If you come from basic understanding of fine-tuning
models, and if you know what is hyperparameter tuning,
transfer learning, and also infrastructure,
that is the sweet spot for you.
You are in a very unique place to exploit
this whole new revolution.
So that is basically the one side of persona,
where you are training the models and fine-tuning models.
But majority offers are going to be on the other side,
where we consume this.
So if you understand observability,
I think you need to understand what is prompt engineering
and how do you feed these observability data,
like [INAUDIBLE] and events to LLMs.
And how do you connect the dots so
that it not only responds with autocompletion
and some remediation techniques, but how
do you connect the dots, and what tools
do you need to close the loop?
For example, when the large language model detects
that the CPU utilization is at its brim
and it needs to add another node, for example.
Now, as an infrastructure engineer,
it's your job to connect the dots
and make sure that you are able to add
one more node to the cluster or enable horizontal power order
scaling.
So that's where there is a lot of opportunity.
Converting the responses given by generative AI into more
of actionable tasks and going for advanced automation,
taking Terraform, Ansible, and all your scripts
to the next level by connecting the dots.
So that is a very unique skill.
So to summarize, there are two things.
One is fine-tuning these generative AI
models for infrastructure and observability data
and consuming those LLMs and connecting them back
to your automation pipelines and scripting engine
so that you are basically getting into an autopilot mode.
PRIYANKA VERGADIA: Yeah.
And then in-- and maybe that second persona
is also responsible for, like, taking the dataset that
is important to train this model,
because the teams that will train the models
are ML experts.
They are not the observability experts.
So it falls on you, because the model is only
going to do as good as the data that you provided,
so that piece would also somehow fall
into the expertise of the observability teams,
in this particular example.
So this is to also represent that any use
cases we see evolve would require,
on top of those foundational models,
would require the experts in each of these fields
that we end up creating the models for to help make
the models better over time with the type of data that we need,
the rich datasets that we need, and then
obviously the expertise to use those models
and consume them in the right way.
So thank you for that.
JANAKIRAM MSV: Absolutely.
PRIYANKA VERGADIA: Yeah.
So looking ahead, what do you believe
is the most exciting potential application of AI
that we have--
that we have yet to see?
I know you mentioned this DevOps use case.
But anything more further, like a stretch,
like five, 10 years from now?
JANAKIRAM MSV: Oh, that's too difficult
to visualize because things are moving so fast.
We can't even predict what's coming up in a quarter
from now.
But-- but in the near future, what I actually visualize
is aggregating multiple LLMs and generative AI models
to solve more complex and advanced tasks.
This is not specific to the AI Ops or the DevOps automation
that I mentioned earlier.
But with generative AI focusing on pretty much every type
of content-- textual content, imagery content,
and video content--
imagine the power of multimodal generative AI, where
you are able to feed the output of one generative AI model
to another and ultimately get what you want.
For example, if you look at e-commerce product managers,
when they want to generate synthetic images
that are representing their product,
and they need to write some kind of a blurb or an abstract
explaining their product and also do some quick computive
analysis and generate a 3D video.
Now, today they have to use a variety of tools
and takes literally weeks or sometimes months
to come out with this output that
can go live on their website.
But with generative AI, the product manager
responsible for the SKU will be able to use one agent that
will orchestrate all these models,
generative AI models responsible for textual content, imagery,
and visual video content, and will come out with a package
that he can straightaway embed in his website.
And that is going to be significantly accelerating
and making a huge difference to product managers who
are responsible for positioning their products.
PRIYANKA VERGADIA: Yeah.
And we just launched this Gen App Builder,
which is sort of in that realm of you
being able to create your own model really quickly
in the voice of, let's say, your PR and your legal terms
and then create this model and then
start to use it in campaigns for social media,
so sort of that end-to-end story.
And it's possible to do that super quickly.
I'm also thinking about another use case, like let's say,
wanting to make cloud easier to use.
You're asking in Chat, how do I build a website--
just a super-simple example.
And then, obviously it's a very open-ended question.
There's multiple things that the AI, like multi-turn AI
conversation you're having, OK, what kind of website do
you want, what's the type of traffic, number of users,
where are they going to be, is it global.
All of those parameters then lead to, OK,
here's the type of architecture that
could work for your website.
And then you end up saying, no, no, no.
I've got a very small team.
And then you're, like, OK, yeah, you've got to go serverless.
So things like that would seem like-- that whole multimodal
aspect of it, of l here's a picture,
then here's a documentation.
And that's kind of where I see the future to be even more
exciting.
And I think we are not too far from that,
which is very, very empowering.
So any last words?
JANAKIRAM MSV: Absolutely.
PRIYANKA VERGADIA: Anything else you want
to add about the future of AI?
What are you excited about?
Anything else?
JANAKIRAM MSV: Yeah.
So just like cloud has evolved and today we
have large hyperscalers with their own APIs
and their own protocols, what I'm
very excited about is how these investments coming
from the hyperscalers would change the world, particularly
the DevOps and the cloud operations model.
I am visualizing a world where we
are going to see agents that are going to be orchestrating
the prompt engineering and fine-tuning and all
of that, which will become the front end.
Just like you have Kubernetes as an orchestrator orchestrating
a set of containers, LLMs are going to be huge.
And we are going to see agents that
are going to be specialized for individual verticals that
will act as a front end and will negotiate with the backend LLMs
and Gen AI models to do what is appropriate
and what is specific to the end user.
So we'll see this evolution of agents taking over
and become the orchestrator of model deployments.
PRIYANKA VERGADIA: Yeah.
And then in between all of that, so LLMs
would make the natural language part of it more easy, but then
also the backend part of it more easy.
And including all this, we would need
this middle layer of the APIs or the middleware that will
actually make things happen.
So if I said, stop this virtual machine
when things like this happen, then
the ability to stop the virtual machine
is through this middleware.
But the LLM is behind it and in front of it
to understand the natural language
and then also understand the operations piece of it.
It's going it's going to be very exciting.
Thank you so much for joining us today, Jani.
It has been incredibly insightful to hear
your thoughts on the impact and the effective use of AI.
We really look forward to seeing how AI will continue and shape
our world in cloud and just in general.
So stay tuned.
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JANAKIRAM MSV: Absolutely.
Thanks for having me.
I thoroughly enjoyed this conversation.
PRIYANKA VERGADIA: Thank you.
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