Connected Social Media

In this video interview, Nutanix cloud native technology expert Dan Ciruli describes the trends and technologies powering an explosion in new applications, particularly those with AI capabilities.

Find more enterprise cloud news, features stories and profiles at The Forecast.

Transcript:

Dan Ciruli: The more we realize the benefits, apps can do this, apps can do that, and the more use cases that over time we have more data, we're gathering more video, audio data, they just enable more things. And so, I have no doubt that as we, you know, data centers become more powerful, the things in our pockets become more powerful, the computers at the edge become more powerful, people have more and more ideas, we'll continue to write more applications than ever, you know, ten years from now we'll be blown away at where, you know, container-based applications are running and what those things are accomplishing, and all of that means, yes, more apps. 

[Related: AI and Cloud Native Spark Explosion of New Apps]

I'm not the first person to make this analogy, but watching AI right now reminds me of late 90s, mid to late 90s internet, and just kind of realizing we don't know all the ways this technology is going to change everything, but it's clear it's going to change everything, right? And that's where AI is right now. It is, you know, let's say it's mid 90s, because it's in that time when you just knew it was going to be incredible, e-commerce hadn't been invented yet, I mean, companies really weren't even figuring out how to market on the internet yet, they were barely putting up websites and taking out, you know, radio ads to tell you to go visit their website, but you just knew it was going to transform, it's going to transform technology and in turn society. AI is at that, you know, it's very early on, but you can already tell there is no doubt, and that in itself will mean lots more apps, right, and AI will be, it'll be in the data center, it'll be in the cloud, it'll be at the edge, it'll be on your phone, it'll be on your watch, it's going to be everywhere, and all of that will be transformative.

[Related: AI, Cloud Native and Hybrid Cloud Fuse to Run Apps and Data Anywhere] 

Virtually all apps that are being written today are running on cloud native, that's true of all the AI apps, right? All of the big companies, they're running all of that AI stuff on Kubernetes, for sure, you can't, I don't know of anybody who's really writing new AI based apps, running those models in VMs, that's all happening in Kubernetes. So in that sense, AI is built on cloud native and is an example of, it's a new application, by the way, and it's one that companies are starting to run in the cloud and just like their other workloads, they're going to want to run on prem, for sure. Your provisioning timelines are different, you've got to buy that hardware and it's expensive, however, that's where your data is, you don't want to send all of your most important data that you need to train models on up into the cloud, so it's another one of those. On the other hand, in a sense they're orthogonal too, because ultimately when you're consuming an application, you don't care if it's running on cloud native, you don't know if it's running on cloud native, when I open up my bank app and I check my balance, that might be running Kubernetes on the background, I don't know, right? Eventually it might be using AI in the background, I don't necessarily know. So in a sense they're orthogonal. One other thing is I think that when we talk about gen AI, it is one of the things that is going to solve the usability problems. We hear a lot about skills gaps and cloud native, and I can't hire people who understand Kubernetes well enough to run it and then be asked to do that. To me that's not a skills gap, it's a technology gap. The problem there is that Kubernetes is too hard to use, and AI is going to be one of the things that solves that. And when we get to what we call intelligent infrastructure, or invisible infrastructure, infrastructure that is using ML to understand, oh, here's a problem that maybe hasn't happened yet, but is about to happen, and here's what we do to solve it. And you can imagine a day, and we're already seeing this, we've already got AI chatbots built into our software that allow you to say, in plain English, why am I seeing this crash loop error, and what can I do about it? And have AI help to solve that problem. So AI will help us bridge that skills gap by making the technology easier to use.

[Related: Making IT Infrastructure AI Ready]

Almost all application development now is happening in containers, in Kubernetes. What happened over the last 10 years, Kubernetes is having its 10 year anniversary, but the first three or four years was very experimental. So more over the last five years was, yes, organizations were adopting it, but they were adopting it mostly in the cloud. Because the cloud vendors, Google, where I used to work, and Amazon, and Azure, were all making it very easy to get a Kubernetes cluster. And so for many organizations, for the last five years, new development efforts were happening in the cloud, and they were happening in Kubernetes, everything on-prem was still running in VM. There's two things that are happening. One is that now even some of the stuff that's being written and run on-prem, they're saying, well, this is new development now, we want to do it in this modern way. And two, companies are rethinking the economics of cloud computing, and realizing that essentially renting is more expensive than buying, and doing all of your stuff in the cloud is very expensive. So they're saying, hey, we like these benefits of running in the cloud, we like this idea of velocity, we can get changes into production faster, but we need to be able to do that on-prem too. And so increasingly, IT departments are being asked, hey, how can you give us Kubernetes clusters? You're the one who can give us VMs on-prem, how can you give us Kubernetes on-prem?

[Related: Building a GenAI App to Improve Customer Support]

Cloud Native started, I'll say it started at Google, although that's not entirely fair. There was a bunch of people at a bunch of different companies that were trying to figure out how to ship software faster, how to make it from the time someone had an idea, put that in code, put that into production, could reduce the amount of time to do that. And at the same time, also make sure that when you did, it would be scalable enough to use at some of these big companies. And Cloud Native is what we use now to describe what they were developing back then, which was a, it's essentially, it's more layers of abstraction between the developer and the machine that the code is running on. Because traditionally, getting a code to a piece of machine involved server installs, and server installs are notoriously complicated and complex, and it would take a long time to get something just installed and running. And with Cloud Native-style architectures, you could put together a pipeline where a developer wrote some code, they checked it into source code, pushed it to the source code repository, and then it kind of automatically and automagically got deployed to a server somewhere, no one cares where, no one's ever going to SSH into that server. So Cloud, and then over time, a bunch of this technology got open sourced or created again in open source, and now when we refer to Cloud Native, we refer to these projects like Kubernetes, that ultimately are aimed at this, and aimed at how can we make it faster for developers to get code into production.

[Related: AI and Cloud Native Alchemize the Future of Enterprise IT]

In the beginning of cloud computing, we would talk about hybrid, we would talk about hybrid as a temporary state, and we really thought that hybrid was going to be how people ran until all of their stuff was in the cloud. And I think now where we are as an industry has realized that hybrid is not a temporary state, it's not a transitional state, this is how most organizations will be running for the next decade, right, for the rest of my career probably, is that, oh no, we will be absolutely using the cloud appropriately, but we are still, we're investing in data centers, we're realizing for reasons of cost efficiency, for sometimes data sovereignty, sometimes just control, there's stuff we're going to want to run here. And so solving hybrid, not as how do we have this temporary bridge, but how do we put in place something that permanently lets us use the cloud and the data center appropriately, taking into account costs, taking into account where the data is, and taking into account sovereignty rules. So that's what's different about hybrid and multi-cloud today, is that companies are realizing this is a permanent state, and how do we make sure that we're putting in place the right structure so that we can take advantage of each of them for what they're good for.

In this video interview, NAND Research Chief Analyst Steve McDowell explains how IT decision makers assess strategies and infrastructure needed to run artificial intelligence capabilities.

Find more enterprise cloud news, features stories and profiles at The Forecast.

Transcript:

Steve McDowell: I think we're all getting AI fatigue. Every product briefing I go to, if it doesn't have the word AI in it, I'm in the wrong room. That's all anybody wants to, whether it's relevant or not. And I think a lot of these technology transitions, whether it's the internet, whether it's the smartphone, whether it's the PC, going way back, there's a brief burst of time where we're very excited about the technology pieces, but the value of technology comes in how I use it. So have we hit a wall on AI? No, but I think it's time to change the conversation. Let's stop talking about GPUs. We'll stop talking about who the providers are, and let's start talking about what are we going to do with it and how's it going to change my business? Because that's the interesting conversation. The way I think about AI and we talk about AI, now I'm going to talk about generative AI, but I'll just call it AI. What's driving the current moment? I mean, we've had AI for a decade, kind of the modern form of AI for a decade. We use it. We use it for predictive analytics, image recognition, retail, whatever. But what's really impacting what's about to impact enterprise is generative AI.

We're seeing a couple of things, and I think 2023 was really the year we figured out how to make these models. And right now we're going through a kind of rapid phase of how do we make it safe for enterprises? And I look forward over the next 18, 24, even 36 months, and it's really how do we deploy that in the enterprise from an IT perspective? That means a couple of things. One, I need to pick a partner who's going to be my generative AI provider. I have a core set of functionality. There's only a handful of companies in the world that could train these models. It's OpenAI, it's meta with Llama, it's Anthropic, just a handful of these companies. And the way it deploys in the enterprise is I take this large language model and I fine tune it with my own data.

So if I'm an IT guy right now, I'm going down the path of how do I, I'm just trying to deploy. I'm trying to enable my enterprise to use it. And then when it comes to using it, there's really two pieces. There's the piece that's very business focused. How am I going to use AI to enable the next or the next iteration of digital transformation? It's going to change all our lives, but it's also, how do I use it to make my own IT operations more efficient? I've been to 15 conferences this year, and the theme for 2024 is AIOps, right? Even Nutanix is announcing capabilities around AIOps.

[Related: Pivot Past the Enterprise AI and Cloud Native Hype]

Do I trust it? How do I trust it, how do I deploy it? Where do I use it? A lot of decisions are happening for the poor enterprise IT architect. So what's happening with ai, we're trying to figure it out. We're at the phase where we've invented the technology right now, we're in the enablement phase, then we're going to start to use it, and then it's going to drive the transformation. And I look at every kind of big technology, every transformational technology kind of follows this path. Although the timelines are getting much shorter, right? It's understand it, it's enable it, it's play with it, and then it's going to drive change.

It doesn't have to be expensive to get into AI as a user and consumer of the technology. And I think where it's disrupting the tech industry and driving and forcing a lot of this conversation about the technology is it's very expensive. It's very complex. It doesn't look like anything I've touched before as an IT guy, or it looks like scientific computing. The cloud guys are solving this for me. They're managing the infrastructure. They're buying these expensive GPUs. They're amortizing the cost over multiple users. Things that I don't have a budget or capability to do as an enterprise, where that's causing disruption in the industry is, well, if AI is driving the industry right now and cloud is taking all those dollars, and my company's name is Dell or HPE or Lenovo, where am I getting my revenue? I mean, the server market was already down. And if those dollars now are being prioritized to cloud, that creates a real dilemma.

[Related: Cloud Vendor Shakeup Puts Focus on IT Resilience]

If I'm looking at this as an IT practitioner saying, where's the value of AI to my enterprise? I don't care about hardware. That's why I like GPT-in-a-Box that the Nutanix is delivering because that's a software set of capabilities. I can deploy that if that makes sense, right? I can deploy that at the edge, if that makes sense, or I can roll that out in the cloud, if that makes sense. So I think a lot of tech companies are trying to prove their relevance around AI. And I'm not saying they're irrelevant. It's just causing a lot of disruption. It's going to change the way that we think about infrastructure.

There are two pieces of AI. There's training and there's inference, right? As a business user, the value is on the inference. An inference is when I take an AI model and I throw some of my own data against it, and it gives me back results, the speed, the time to value, the time to decision is the AI. I say, the closer to your data that the AI is, the faster I'm going to get time to value. And where we look at where, and this is not even a generative AI thing so much when we talk about AI, the most prevalent use of AI is image processing, whether it's for manufacturing, whether it's for retail, whether it's for automotive. If you have a car made in the last 10 years, you have so many sensors in your car, it's not efficient to take the cameras in your car or the cameras at the seven 11, send those up to the cloud to be processed and send them back down.

[Related: Living Workflows of AI at the Edge]

If I'm running a retail establishment, I might not have internet. It may go down, there may be a storm. I can't shut my business down when I lose the internet. So by moving those inference functions to the edge, I get all the value of that AI where it makes the most sense. And there's a couple of things that came together to make this the moment in time where that happens. One is we've been talking about the value 5G is going to bring to the world. I get all of this high bandwidth wireless everywhere. We rolled that out without really a killer application. And then we started all this generative AI stuff, and we got really good at AI. And now part of what that did was all the focus on high-end ai, kind of the natural curve of technology is all of the AI inference capabilities required for mid-range, low level ai, dirt sheet, dirt sheet, right? The camera you recorded me on right now can probably track my face. It's doing inference and facial recognition on a $30 processor. It's AI at the edge, it's practical, and it brings true business value. Now, where it gets complicated when we talk about edge, edge is anything outside of the data center where there's no IT guy, let's call it that.

[Related: Building a GenAI App to Improve Customer Support]

It's a couple of edge segments. There's robo, remote office branch office. To me, those are extensions of your data center. They're well controlled environments where it becomes really interesting or kind of mass deployments, whether it's a convenience store, whether it's factories, whether it's smart cities, and I have sensors on the lights for traffic control. Managing all of those becomes a challenge. So I'm deploying AI at the edge for these kinds of use cases, but I also have to manage security patches, updates. I got to push new models down. Sometimes I got to bring data back. So what's the framework look like for that? Well, guess what it, it's not that different from how I'm doing hybrid multicloud. The boxes are just a lot smaller. So we did this paper and it's like we're driving AI to the edge for all the reasons I just said. But then it was really more about, okay, we're pushing AI to the edge. How do we manage it and how do we make it efficient to manage? And that requires, again, it looks a lot like multicloud, but it may be a more unconstrained environment.

Over 700,000 people die by suicide each year, many of whom are active participants in the global workforce. For employers, this is more than a statistic—it’s a call to action.?In this episode, Dr. Dan Reidenberg, Managing Director of the National Council for Suicide Prevention, reveals why employers must take action on suicide prevention. He also shares the significant financial and operational toll that untreated mental health issues bring to the workplace — $50 billion in annual costs and 68 million lost workdays — and how employers can foster a compassionate culture to reduce the stigma, help individuals better recognize warning signs, and provide access to effective care.?

Guest: Dr. Dan Reidenberg, Managing Director of the National Council for Suicide Prevention

In this Tech Barometer podcast segment, Nutanix President and CEO Rajiv Ramaswami talks to tech reporters about why IT leaders turn to Nutanix to counter changes they face after Broadcom acquired VMware.

Find more enterprise cloud news, features stories and profiles at The Forecast.

Transcript:

Rajiv Ramaswami: We've been competing against VMware. Yeah, it's a good company. We competed some. We won some. We lost some. Obviously, you can't foresee what's going to happen. Could we have foreseen that Broadcom was going to acquire VMware? No, I don't think so. But now that it's happened, there is an unexpected opportunity. Our view is to continuously evolve as a company by investing in innovation and being where our customers need to be over time and not just stay still. And if you stay still at some point end up having to be optimized because there is no more growth left.

Jason Lopez: In this story, you're going to hear the voice of Nutanix CEO, Rajiv Ramaswami recorded in a conversation with journalists. This was not the typical press briefing of the speaker at a podium with prepared remarks, but rather he sat at a table with tech reporters in an open-ended back and forth. This is the Tech Barometer podcast. I'm Jason Lopez. The journalists led the discussion, which zoomed in on Nutanix's view of Broadcom’s acquisition of VMware.

Rajiv Ramaswami: They acquire companies that are fairly mature, that they have good market share, that have good products and fairly sticky ones, and then they do what they need to do to maximize the profitability of those companies over the next three years. And their model is one of continuous acquisition. Acquire, optimize, acquire, optimize, right? Because then that's the model. So you keep acquiring, you keep optimizing. So they've done well with that model so far. They've done very well for their shareholders.

[Related: Cloud Vendor Shakeup Puts Focus on IT Resilience]

Jason Lopez: Companies go through stages. Ramaswami pointed out such as an incubation period, then scaling, then maturity. He said VMware had arrived at that stage.

Rajiv Ramaswami: Their growth was slowing. They had kind of largely saturated their markets that they were playing in. vSphere had what, 80% plus share in the market? That's the stage. That's when you start thinking about optimization. You cannot grow anymore. So if you were to take us and you do the same squeezing what we got, 25,000 customers, we still have huge amount of opportunity to grow and gain share. 

[Related: Slew of Changes Drive VMware Customers to Consider Alternatives]

Jason Lopez: For comparison. Ramaswami explained that Nutanix wouldn't be a good target for an optimization strategy. It still has a high ceiling for growth, meaning it can essentially double its customer base.

Rajiv Ramaswami: That's what we are out to do. So we are still in the scale phase and we've told our investors, we are profitable, we are growing. So it's a different status.

Jason Lopez: One of the things Nutanix has been able to do and continues to do is to adapt to the market. The company started with a hardware focus and has shifted to software infrastructure,

Rajiv Ramaswami: But that's not where we want to be tomorrow. But if you look at the future, the future is around being a platform for modern applications. And that's why we bought D2iQ, for example. We are investing and expanding our platform offerings. We are investing in cloud native AOS for containers. I think as a company we are in a better place to evolve our customers to the next stage building and running modern applications on these platforms.

[Related: AI, Cloud Native and Hybrid Cloud Fuse to Run Apps and Data Anywhere]

Jason Lopez: As the conversation continued, Ramaswami began talking about the realities of customers making the switch from VMware to Nutanix. The bottom line is the rise of VMware pricing.

Rajiv Ramaswami: The vast majority of VMware customers today are running on vSphere only, and those customers mostly are not perpetually (licensed). And so those customers are not being forced to buy just a subscription, but the full bundle. Customers have to buy the full VCF stack.

[Related: Existing IT Hardware Gets New Path to Hybrid Multicloud]

Jason Lopez: In addition to the issue of being locked into a subscription model with rising costs. There are other questions.

Rajiv Ramaswami: What's the level of innovation going to be? What's the level of support going to be like if the model is to double the ebitda, the profits within three years, which is what they've said publicly, what would happen there?

Jason Lopez: Another aspect of switching the VMware environment versus the Nutanix environment. An engineer who has worked with VMware tools for 20 years will have to relearn some new ways of doing things. But Ramaswami pointed out, Nutanix is built for adoption. Its processes are easier to learn.

Rajiv Ramaswami: It's not a huge learning curve, but it is not exactly the same. We do a lot of things differently. In some ways, many of them simpler, and we also have a lot more focus on the data side of the equation compared to VMware.

Jason Lopez: And finally, Ramaswami addressed the question of the VMware stack of the hypervisor connected to three-tier storage.

Rajiv Ramaswami: In the past when people chose Nutanix, they had to adopt hyperconverged (infrastructure or HCI). And a lot of VMware is not hyperconverged. They're hyperconverged, but the big chunk of their rest estate is really three-tier storage, right? Hypervisor connected, three-tier storage. And so that conversion, we were doing it at a certain pace in terms of converting that three-tier, right? HCI. That's how we grew as a company. That's what we did. And now we have an opportunity to go back in and also potentially just replace a hypervisor and get a faster entry. We have seen a huge amount of engagement increase in customers, so lots more customers concerned and upset and talking to us. Many of the customers actually signed multi-year deals with VMware before the acquisition closed. And so that gives them three to five years to do whatever they need to do. And so they're not in a rush to necessarily do anything at this point. 

[Related: Seeing AI’s Impact on Enterprises]

Yes, a lot of them are kicking the tires. They're testing our product, but to go from there to a sale and actual migration takes time. So they're not in a rush. The second factor is the hardware, right? At what point in time have they invested in hardware? When does it come for refresh? Because that's the time at which you can actually make the migration to hyper converge because it's an architectural choice. And the third point, I'll tell you, there is much more awareness of what would happen, but it depends on the country and where you're in. For example, I was in Japan recently, and a lot of Japanese customers were just hoping that everything would be fine because they believe in these long-term relationships and they believe and trust them. And now they've seen what's happening and now they're concerned. But it took them a year, and they did nothing. Right? And now they're starting to think about, okay, maybe I should have a plan B. So it takes time. Have we seen a big spike in our business? Immediate business as not yet? We've brought on investors that this is going to be a multi-year journey.

[Related: AI and Cloud Native Alchemize the Future of Enterprise IT]

Jason Lopez: The core of Ramasswami's message is that Nutanix and VMware within Broadcom, though they compete in the same space, have different agendas. Nutanix is aimed at growth. Broadcom is aimed at billing an existing customer base.

Rajiv Ramaswami: That's their model. So they're actually going down a true and trusted strategy. Typically, private equity companies do this, but they're doing this in the public market and doing that very well, I should say, executing on their strategy. I just want to say that's not our strategy. I mean, our strategy is to grow, continue to drive innovation, and focus on building these long-term relationships with customers.

Jason Lopez: Rajiv Ramaswami is the CEO of Nutanix. This was recorded at the 2024 Next conference in Barcelona, Spain. This is the Tech Barometer podcast. I'm Jason Lopez. Thank you for listening. Tech Barometer is produced by The Forecast where you can find more podcasts, articles, and videos about the technology story and the people in tech. It's at the forecast by nutanix.com. That's The Forecast by Nutanix, all one word. Dot-com.

A full 35% of U.S. counties are maternity care deserts—regions that lack birthing facilities or obstetric clinicians, according to recent March of Dimes report, Nowhere to Go: Maternity Care Deserts Across the US. Alarmingly, this may worsen as maternity units close nationwide and provider shortages persist, a trend that will further exacerbate poorer pre-pregnancy health, inadequate prenatal care, and higher preterm birth rates.

In this podcast episode, Dr. Amanda Williams, Interim Chief Medical Officer at March of Dimes, explores the rise of maternity care deserts, the impact of doulas and midwives on maternal mortality and the strategies for improving patient access to care. Dr. Williams identifies ways that employers and others can help to improve maternity care by providing access to digital solutions and community resources, advocating for reimbursement reforms, and supporting value-based care models.

Guest: Dr. Amanda Williams, Interim Chief Medical Officer at March of Dimes

In aggregate, employers are the largest payers of health care in the U.S. and subsidize other health insurance markets, yet they are often sidelined from the crucial conversations around health care policy and delivery matters. With another spending surge on the horizon, it is more important than ever for employers to lean into redefining care access and delivery.

In our latest podcast episode, we talk about this and more with Paul Keckley, health care policy analyst and managing editor of The Keckley Report. We explore his article “Are employers ready to move from the back bench in U.S. health care?” and discuss the transformative actions that will drive meaningful change - a stronger voice and increased activism in delivery, the integration of social services and strategic engagement.

Business Group on Health consistently offers insights on the critical role employers play in shaping the health care industry. Learn more about our perspective here.

Guest: Paul H. Keckley, Managing Editor of The Keckley Report

For individuals that struggle with mental health, the intensity of the challenges often evolve over their lifetime due to triggers or phases of life.

In this podcast we speak with Dr. Karen Swartz,psychiatrist and professor from the Johns Hopkins University School of Medicine, about the continuum of mental health and common triggers, including those less often addressed, like regret and loss. Dr. Swartz also shares insight into the youth mental health crisis, the “dangerous levels” of stress that 41% of parents are experiencing, and more.

Tune in to learn how Dr. Swartz’s experience and guidance can help employers more deeply understand the mental health challenges their employees and families are experiencing and better provide meaningful support.

Guest: Dr. Karen Swartz, Director of Clinical and Educational Programs at the Johns Hopkins Mood Disorders Center, Professor in the Department of Psychiatry and Behavioral Sciences at the Johns Hopkins School of Medicine, Founder and Director of the Adolescent Depression Awareness Program (ADAP)

Thousands of Intel’s employees connect to the corporate Wi-Fi network every day. When connectivity issues occur, both productivity and user experience (UX) suffer. Intel IT makes every effort to optimize the network’s performance. Still, until recently, we had little visibility into client behavior through traditional network monitoring solutions because they report issues only from the network infrastructure perspective. At the same time, endpoint IT tools are limited to operating system data.

Based on its existing market presence with PC Wi-Fi products, Intel’s Client Computing product group developed Intel Connectivity Analytics technology. The product group collaborated with Cisco—a well-known enterprise Wi-Fi network infrastructure supplier—and Intel IT to develop compelling use cases and solutions that utilize Intel Connectivity Analytics. Intel IT provided product feedback and practical applications of the technology in our enterprise environment and helped optimize the use cases over Intel’s network infrastructure. Our input helped to better define the data and analytics needed to achieve the desired business outcomes, such as faster troubleshooting, mean time to repair (MTTR), reduced network total cost of ownership, and a better UX.

Intel IT now uses Intel Connectivity Analytics—delivered through the Cisco Catalyst Center (previously Cisco DNA Center) and cloud dashboard—to improve the management of Intel’s Wi-Fi network. We are also working with the overall ecosystem to expand the use cases for Intel Connectivity Analytics and further enhance AI for IT Operations (AIOps). We have reduced some client-side troubleshooting from upwards of 15 minutes to 10–15 seconds. Similarly, finding the root cause for ubiquitous network issues—which could take days—now often takes seconds as well.

We encourage other IT departments to consider deploying IT tools powered by Intel Connectivity Analytics for Wi-Fi, which is built into Intel Wi-Fi adapters and requires no software installation or maintenance. It can be utilized by Cisco and other Intel Connectivity Analytics Program members.

In addition, we are now working with the Intel Client Computing product group to develop an additional Intel Connectivity Analytics offering that shares data through a PC agent to the cloud and expands the available connectivity analytics beyond Wi-Fi to include Thunderbolt technology, Bluetooth, Ethernet, and more.

In this Tech Barometer podcast segment, McDowell shares insights from his report Taming the AI-enabled Edge with HCI-based Cloud Architectures and explores the impact of extending IT resources to the edge and the driving force of AI.

Find more enterprise cloud news, features stories and profiles at The Forecast.

Steve McDowell: The reason we push AI to the edge is because that's where the data is, you know, we want to do the processing close to where the data is so that we don't have latency. And in a lot of environments, if we're ever disconnected, it's going to shut down my business.

Jason Lopez: The question is, how do you deploy edge resources in real time? In this Tech Barometer podcast, Steve McDowell, Chief analyst at NAND research talks about his paper "Taming the AI-Enabled Edge with HCI-Based Cloud Architectures." I'm Jason Lopez. Our aim in the next several minutes is to discuss how AI impacts edge computing.

Steve McDowell: We've always defined edge as any resources that live outside the confines of your data center. And there's some definitions that say the extension of data center resources to a location where there are no data center personnel. It's remote.

Jason Lopez: But AI, of course, adds complexity. One example McDowell cites is automated train car switching. The sides of train cars have bar codes which are scanned, and a local stack of servers processes where the cars are and where they need to be.

Steve McDowell: I can do this in real time. I can partition my workloads so that, you know, computationally expensive stuff or maybe batch stuff can still live in the core. And I don't have to do that at the edge all the time. So I can really fine tune what it is I'm deploying and managing.

[Related: Slew of Changes Drive VMware Customers to Consider Alternatives]

Jason Lopez: This is important when you consider that AI at the edge differs from traditional edge deployments primarily due to its need for greater computational power.

Steve McDowell: Once we start putting AI in, then suddenly we have to have the ability to process that AI, which often means the use of GPUs or other kinds of AI accelerators. Ten years ago, if we talked about edge, we're talking largely about embedded systems or compute systems that we treat as embedded. Embedded is a special word in IT. It means it's fairly locked down. It doesn't get updated very often. When we look at things like AI, on the other hand, that's a very living workflow. If I'm doing image processing for manufacturing, for example, for quality assurance, I want to update those models continuously to make sure I've got the latest and the greatest.

Jason Lopez: And along with managing fleets of hardware and software in AI deployments at the edge, there's also the issue of security.

Steve McDowell: By treating edge systems as connected and part of my infrastructure, and not as we historically have treating them as kind of embedded systems, if you will, it also allows me to, in real time, manage patches, look at vulnerabilities, surface alerts back up to my security operations center, my SOC. It makes the edge look like it's part of my data center.

Jason Lopez: Tools like Nutanix allow for this approach, applying a consistent management practice across both core and edge environments. This involves deciding what tasks to perform at the edge versus the core due to constraints like cost, security, and physical space. 

Steve McDowell: A key part of the conversation becomes what lives where? And that's not a tool problem, right? That's kind of a system architecture problem. But once you start partitioning your workloads and say, this certain kind of AI really needs to be done in the core, Nutanix gives me that ability and cloud native technologies give me that ability to say, well, I'll just put this kind of inference in the cloud and I'll keep this part local.

[Related: Pivot Past the Enterprise AI and Cloud Native Hype]

Jason Lopez: McDowell's thinking springs from the flexibility afforded by hyper-converged infrastructure. The idea of AI at the edge is part of the whole architecture of storage, network and compute.

Steve McDowell: That can be as disaggregated as it needs to be. So if I need a whole lot of compute in the cloud, I can do that and then put the little bit at the edge and I can manage all of that through that single pane of glass, very, very powerful.

Jason Lopez: Treating edge computing as a part of the data center becomes so interesting because of how the data center itself is being transformed by AI and machine learning.

Steve McDowell: Once we abstract the workload away from the hardware, I've broken a key dependency. I don't have to physically touch a machine to manage it, to update it, to do whatever.

Jason Lopez: The point McDowell makes is how management, not just of the configuration of a node, but across a fleet, is simplified. It enhances efficiency and scalability.

Steve McDowell: We're taking technology that evolved to solve problems in cloud, but they apply equally to the edge, I think. It turns out, it's a fantastic way to manage edge.

[Related: More Reasons for HCI at the Edge]

Jason Lopez: AI at the edge is increasingly adopting cloud-native technologies like virtualization and containers. The shift is to container-based deployments for AI models, sharing GPUs and managing them remotely.

Steve McDowell: If you look at how, you know, NVIDIA, for example, suggests pushing out models and managing workloads on GPUs, it's very container-driven.

Jason Lopez: And McDowell explains why this simplifies edge management.

Steve McDowell: A GPU in a training environment is a very expensive piece of hardware. And giving users bare metal access to that, you know, requires managing that as a separate box. Using Cloud-native technologies, I can now share that GPU among multiple users, very, very simply. That same flexibility now allows me to manage GPUs at the edge with the level of abstraction that works. So I can sit in my data center, push a button and manage that box without actually worrying about what that box looks like necessarily. So I don't need that expertise kind of onsite, right? Which is a key enabler for edge. If you have to have trained IT specialists wherever you're deploying, that doesn't scale. And edge is all about scalability.

[Related: The Future of AI Computing Resides at the Edge]

Jason Lopez: GPUs are typically what power AI, but are are not commonly found at the edge. But inference is a facet of AI that many technologists see value in at the edge. GPUs would be the right fit if at the edge, generative AI is needed. But what's needed now are inference engines, especially around vision and natural language processing.

   

Steve McDowell: Take, for example, a retail environment where they have intelligent cameras that are positioned all up and down the aisles of the grocery store. And the only job that these cameras have is to monitor the inventory on the shelf across from the camera. And when they've sold out of Chex mix and there's a gap there, it sends an alert, come restock. I mean, it's very kind of data intensive and you don't want to send that to the cloud necessarily.

Jason Lopez: Technology is moving toward managing infrastructure environments seamlessly, such as edge, data centers, and cloud, without changing tools or management models.

Steve McDowell: Nutanix has capabilities for managing AI in your workflow, kind of period, full stop. A good example of this is GPT in a box. Where it's a technology stack and I plug a GPU in and I can do natural language processing. If I want to push that out to the edge. I don't have to change my tools. I mean, the beautiful thing, and the reason that we use tools like Nutanix is that it gives me kind of a consistent control plane across my infrastructure. Now, infrastructure used to mean data center, and then it meant data center and cloud. And now with edge, it means data center and cloud and edge. The power of Nutanix though, is it allows me to extend outside of my traditional kind of infrastructure into the edge without changing my management models. So, as AI goes to the edge, I think the things that already make Nutanix great for AI in the data center are equally applicable at the edge.

Jason Lopez: Steve McDowell is founder and chief analyst at NAND research. This is the Tech Barometer podcast, I’m Jason Lopez. Tech Barometer is a production of The Forecast, where you can find more articles, podcasts and video on tech and the people behind the innovations. It’s at theforecastbynutanix dot com.

In this Tech Barometer podcast, Nick Mahlitz, digital infrastructure manager at Forestry and Land Scotland, takes listeners to his homeland, where he helps the government use data and cloud technologies to manage natural resources and meet sustainability goals.

Find more enterprise cloud news, features stories and profiles at The Forecast.

Nick Mahlitz: I learn new things every day as I talk to our staff members. Just recently, we're using drones with lasers to map out the land. So the drone footage stuff is really, really, really good. They can then tailor what they do with the land around what they found with these drones and with the lasers, they can go through forest layers, they can analyze the types of land that is, and oh, there's a ridge there that we perhaps need to avoid, and just making better decisions.

Jason Lopez: We're starting this podcast interview by parachuting right into the heart of the work of Forestry and Land Scotland, the federal organization that manages the country’s public land and wilderness. A year ago we talked with the manager of the organization's data centers, Nick Mahlitz and posed the question:

Ken Kaplan: The forest needs technology?

Nick Mahlitz: Yes. The forests do, yes, to manage your forests well and good, to use technology in a challenging environment like Scotland, where it's very remote and the weather can be quite extreme sometimes. Technology and exploring all realms of technology will only help us better manage Scotland's forests and our land.

[Related: Forestry and Land Scotland Trailblazes Private-Public Shift to Cloud]

Jason Lopez: So, we circled back to follow up on that interview. Forestry and Land Scotland is utilizing technologies like drones with laser mapping capabilities. As we'll learn, it gets very data intensive which is Nick's job to oversee. He supports the organization's mission to balance natural resources. One of those balancing acts is to better track wildlife, particularly deer, to protect young trees. 

Nick Mahlitz: Scotland has a lot of deer, so we have to cull a fair amount of them. And the challenges around Scotland being a very remote piece of land and identifying and culling enough deer in a small frame of time can be very challenging. So with technology of tagging deer and using drones to manage where they are, a ranger can go from culling a couple of deer and say half a day or a day to 8, 9, 10 deer within the same timeframe.

Jason Lopez: Scotland's public land serves many interests. Trees enhance carbon sequestration. Some of its forests are grown for timber, and some of its land is used for renewable energy projects -- such as wind and hydroelectric power. Scotland's goal is to be net zero by 2045. One of the most important uses of the land is for the public's enjoyment of the outdoors. He takes note: it's good for people behind the scenes of his own organization to experience the places their work supports.

Nick Mahlitz: You know, I don't want to be the person in the basement. You know, the data is there, but it's also good out and enjoy it. And we try and do that. We encourage non-forester staff to go out with a forester for the day. Pick what you want to do. Do you want to go and see a piece of bog peatland be restored? Do you want to go and plant a tree? Do you want to go and uplift trees? You know, we offer these activities to people like myself or back office staff, HR procurement, except anybody who wants an interest in it, because it's so important. If you understand that you'll understand your role in the organization and how better you can play a part.

[Related: How a Top University in Scotland Expanded Remote Teaching Tech During a Crisis]

Jason Lopez: This is why the forest needs technology. The earth's landscape has been so altered by human development for the past 30,000 years, it requires human conservation to prevent further decline or restore land to a balanced state. Intervention is critical.

Nick Mahlitz: Peatland restorations, the soil gets degraded, you know, so we're trying to restore the balance in the soil so that it can keep more carbon, et cetera, et cetera. So there's that ongoing. We've got big plans for a big nursery that's coming up where we can plant 19 million trees a year. We're planting the trees in a special way. The seeds are planted into biodegradable paper that can just be streamlined, planted, and we can plant far more than we could normally. And then as we cultivate them in a nursery, we have to plant them outside in fields. And we've re-engineered equipment to then do that planting for us rather than manually. And, you know, just that kind of approach just means far more efficiencies meeting our targets and making our staff more efficient, which is fantastic.

Jason Lopez: What scientists are learning today about the natural world and what technologists are innovating is accelerating.

Nick Mahlitz: And that is unlocked by cloud, by edge, by modern approach. Lots to be done though. Lots to be done still.

Jason Lopez: Nick's current goal, managing the data centers of Forestry and Land Scotland, is on integrating AI and automation to improve their operations to better understand their data.

Nick Mahlitz: And that's what we're actively working on now. And the reality is I can know about AI and automation, me and my team and others in the digital landscape. We know about that for ourselves, but to translate that into somebody with a chainsaw who's cutting down trees or somebody who's managing wildlife management as in deer or our nurseries where we're planting our seedlings to then grow, what does AI and automation do for these ones? And that's the core of our business.

[Related: Do Forests Really Need Technology?]

Jason Lopez: He reminds us that AI is not the core of the business. AI is a tool. His team's core business is about data.

Nick Mahlitz:  How much data do we have? How can we better understand it? How can it better help us make more informed decisions on our future for sustainability, for a net zero, for generating revenue, et cetera, that we do? So there is now a big piece to understand how we capture data, how we store that data, how we report on that data, how we integrate that data, how we use AI and automation with that data. So that really is a big focus for our organisation over the next year or two.

Jason Lopez: And this exemplifies how Forestry and Land's data centers operate, with a passion for sustainability. Migrating to the cloud improved efficiency and prompted a shift towards reducing energy and CPU usage ,as well as modernizing.

Nick Mahlitz: So, you know, we have, we have legacy systems and they have legacy interactions and, but they're now in the cloud. So we can replace some of those interactions with more modern solutions, which gets rid of, you know, having to spend so much energy or CPU, I get rid of that inefficiency, even in code, even right down to the lower levels. And that really can make a big difference as well. And that's something that's, FLS is passionate about sustainability, but even, you know, filtering that down to our digital teams, they appreciate that too. So that's where we can look at and make those changes that just makes everything run better in a more sustainable way.

Jason Lopez: The transition to a full public cloud makes management easier and has simplified data center operations. And it's allowed for easy integration of other cloud products and solutions.

Nick Mahlitz: So we have no on-prem environment to administer or manage. So that is quite a unique position that we find ourselves in. And we could not be more delighted with the results. The actual transition to the cloud using Nutanix was an experience that made our journey so much simpler and has bought us the time that we need to modernise and transform our solutions into that next-gen approach. So what we did with Nutanix really was, we saw it as groundbreaking. It's delivered what we aspire to and it had benefits that perhaps we never really realised at the time that we now enjoy.

Jason Lopez: Nick says the migration has resulted in something else they didn't originally factor in: time savings. He and his colleagues have the time resources to invest in new technologies, which help in the goal of reaching that net zero target by 2045.

Nick Mahlitz: And comparing our footprint of our on-prem environment compared to what we have now through the metrics that we receive from Microsoft, given that we're in the Azure platform, it's really heartening to see that that sustainability piece and net zero is being reached in some way or added to our targets that we have as an organization. And then equally that full cloud integration means that we can now tap into other cloud products, cloud solutions, and very easily integrate them into what we have, which before we didn't fully appreciate that we could do that. And as Nutanix expand their products and services in the cloud, we're only going to enjoy that more and more.

[Related: Easy Alternative for Migrating or Extending to Public Cloud]

Jason Lopez: The transition to the cloud, again, enabled the organization to adopt other cloud technologies.

Nick Mahlitz: And now that we're in there on NC2 in the cloud, what we had to do as part of that journey was unlock other cloud technologies to help us realize that. So for instance, identity management, access management, VPN, et cetera, et cetera. We're now using all the cloud variants of such so that we have no dependency on really local on-prem infrastructure. So what that entails is a very much a kind of zero trust model that's highly secure within line with modern approach. So that really can then unlock capabilities that we're just starting to tap into. But given my team, the ability to now manage that in a completely different way to what we had. But it unlocks really future capabilities at which the IT person's appetite and desire and helps for recruitment and retain of staff and investment and development on staff when what we've done can excite people. It can excite the IT people for sure and other people in the organization. So that's been really, really a good thing for FLS.

Jason Lopez: Astrophysicists remind us what it is to look back, from space, at planet earth, and viscerally understand how vital earth is to life as we know it, yet how small and fragile. People across political boundaries, economies and cultures are galvanizing efforts to preserve oceans, land and forests… and doing this whether its reducing pollution, conserving farmland topsoil, or establishing more efficient data centers.

Nick Mahlitz: And it really just lines up to understanding where we are in technology and our timelines and our lives, working in that agile manner, having the growth mindset, embracing technology, all the attitudes that really permeate behind a good digital team. We took that novel approach. We did our due diligence, but we did something new and exciting. And really that's the refreshment I have from working for 20 years in this career, so to speak, that still there's the ability to do novel, new things. We Scots are a passionate people. And as I meet other digital and IT teams in other government areas, there's a similar aspiration and enthusiasm for understanding technology in their, in their areas too.

Jason Lopez: Nick Mahlitz is the senior digital infrastructure manager for Forestry and Land, Scotland. This is the check barometer podcast, I'm Jason Lopez. You might want to check out the original video we did with Nick entitled "Do the forests really need technology?" You can find that and more stories and podcasts at the forecastbynutanix dot com.