AIOps

85 different people wrote it, 8,500 lines each.

Help me understand, how is that going? And I'll tell you more about how the AIOps goes because there's a lot of components to it.

But tell us about your current experience.

What are we facing right now?

So you've got to be quick, you've got to be right, you've got to get it done, and you've got to build that confidence in there.

So your automation, your operations, you know, they even talk about AIOps, and it's great to know AIOps because that's when things fail, but that's not when things evolve.

And that's where automation helps you.

You have to be able to evolve your network, and this is a key aspect of it.

So thank you.

Jason, AIOps deploys agents inside the software a business already uses.

like finance, sales, HR operations, engineering, you name it, aiming to get each person back five to 25 hours a week.

Why did you choose to embed agents in existing tools rather than asking companies to adopt yet another platform? And where are you seeing the biggest time savings?

And whether it works or not at the end, just like that McKinsey study, like very small fraction of companies is actually working.

Whether it works or not, they're still paying the costs of implementing that.

The difference with what we're offering with our AIOps product is we're bearing all the risk.

We're funding the implementation, the deployment, the pilot of it.

In the meantime, I am pleased to welcome Kyle Baxter and Sridhar Keturi from sponsor HPE, and we're going to talk about automating enterprise data center networks.

Now, in particular, I'm going to probe our guests about how HPE is integrating its Apstra and Mist products to bring AIOps and self-driving capabilities into data center operations.

We're going to talk about how HPE is leveraging the different strengths of each platform, how the integration can reduce the data collection tax that network operators have to pay, benefits like predictive analytics, and some real-world examples of how customers are using the platform, and more.

Kyle Baxter is Director of Product Management at HPE, and Sridhar Khatri is VP of Engineering.

And it builds that network based on validated designs that we're running every night.

We're running millions and millions of tests every night on all the different permutations and validating that, yes, you're going to get the right configuration, the right intended outcomes based on your specific needs.

So given that, that Apstra was essentially designed to kind of automatically help keep the data center network aligned with these outcomes, why would I want to bring in missed AIOps into the picture? What is the benefit or the driver for a data center operator?

Yeah, that's a great question.

Yeah, one of

the people we talked to, only one, was like actually really deploying AIOps.

And they were saying that they were using it to help out with like, if an application fails, they were like, well, in real time, the AI is going to look at the traffic and the firewall logs and what packet was dropped and have we made any recent changes and whatnot.

And it's able to go through all those, however many tools that it needs to do and do it all right away.

But everyone else was like, yeah, no, we're not there.

And that was seen with what we were talking about earlier with that bandwidth demand.

That was like one of the companies that had the higher bandwidth demand because they'd already deployed AIOps.

But all these other ones were like, no, we're still using people and managed or co-managed services.

So how does AI ready assurance become predictive instead of reactive?

Yeah, I mean, that's, I think, the big point of AIOps in general, right, is how do we make sure that we are, all the things we just talked about, all those metrics we just talked about, right, how are we looking at those metrics and understanding what they're telling us and allowing ourselves to be predictive, right, to say, okay, let's look at where this is trending, let's look at how it's different than normal, right, let's look at patterns and correlation of this data, let's bring that together and try and understand what that's telling us that before it's impacting customer SLAs or the customer service and so on and so forth, right? So it's really about, again, all those different signals and metrics I've just been talking about for the last few minutes, trying to bring those things together, analyzing the way that allows customers you know, our customers to get in front of them and say, okay, you know, this isn't about a troubleshooting.

I mean, troubleshooting obviously still happens, but it's, you know, instead of it being a troubleshooting exercise, it's, hey, let's pull the signal out of the noise and be able to say, hey, here are some key metrics that are going the wrong way.

It's going to impact these services.

And this is something that you need to get in front of.

That's kind of, you know, an approach we're trying to take, I guess, with AIOps.

But I mean, really, however, though, at the end of the day, the service provider customers, enterprise customers, and then their subscribers don't care about packet loss.

They just care about that their application's working, right? So how do we shift from monitoring the network to monitoring the user or subscriber experience?