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Our low-cost IIoT manufacturing use cases create savings & reduce downtime. We hope they inspire other manufacturers to adopt a similar approach.

In this video, Ryan McBrearty, the senior IT infrastructure engineer at global specialty insurer Markle, tells how his team uses Nutanix software and customer services in their quest to cut costs and simplify IT operations.

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

Transcript:

We wanted to cut down operational costs and use automation as a key focus in that. An objective was given to everyone to figure out what processes do we use on a regular basis and how can we speed up those processes and really automate them, especially in the server build space and taking a process that takes two months that has as quick as a window as possible, whether that’s two hours or four hours, beginning with we just had, we want to automate server builds so that we have consistency across that platform. So it was really taking that process and putting a workflow around it.

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

Automation is a key foundation, so we want to automate everything. If there’s a process that is repeated more than twice, then let’s see what we can do to automate it. So that’s how we’re going to continue to leverage Nutanix self-service. So that and the integration pieces that it has with the other tools in our environment, whether it’s networking, whether it’s security, whether it’s cloud, being able to touch all of those things. Our backup team as well, we want to work towards that software-defined data center. 

So there was a lot of back and forth and things had to touch hands multiple times. Whereas once somebody puts in a request, now all of that information is gathered at the beginning, it’s put into at launch of the blueprint and then we just hit go and it just builds it. That shrinks down that timeframe from, again, two months to two hours. 

The beauty of self-service is that it will bend to your whims. So if you are comfortable with writing code in a certain language, whether it’s Python or PowerShell, it will allow you to do that the way that you want. The different pieces were very complicated and very challenging to learn and manipulate, and it took a lot of time to go through and really understand how to use those tools where I think the tools today from Nutanix are a lot easier, a little bit more straightforward.

Support with Nutanix has always been great. NCM [Nutanix Cloud Manager] self-service has really been the easy button for us when it comes to automation. It’s been a great tool that has endless possibilities that we have barely begun to scratch the surface on how to use it and how to leverage it. The deeper we dive into it, the more things that we realize that we can do, and I’m excited for the future of what that looks like because again, the possibilities are endless.

Increasing Electronic Design Automation (EDA) performance and throughput is critical to Intel’s silicon Design engineers.

Silicon chip Design engineers at Intel face ongoing challenges: integrating more features into ever-shrinking silicon chips, bringing products to market faster and keeping Design engineering and manufacturing costs low. Design engineers run more than 273 million compute-intensive batch jobs every week. Each job takes from a few seconds to several days to complete.

As design complexity increases, so do the requirements for compute capacity, so refreshing servers and workstations with higher-performing systems is cost-effective and offers a competitive advantage by enabling faster chip design. Refreshing older servers also enables us to realize data center cost savings. By taking advantage of the performance and power-efficiency improvements in new server generations, we can increase computing capacity within the same data center footprint, helping to avoid expensive data center construction and reduce operational costs due to reduced power consumption.

To meet these engineers’ computing capacity requirements, Intel IT conducts ongoing throughput performance tests using real-world Intel silicon Design workloads. These tests measure EDA workload throughput and help us analyze the performance improvements—and in turn, business benefits offered by newer generations of Intel® processors.

We recently tested two-socket servers based on the Intel® Xeon® Platinum 8400 and Gold 6400 processor Series, operating single- and multi-threaded EDA applications running Intel silicon Design workloads for more than 113 hours (about 4 and a half days). Select results include the following:

Based on our performance assessment and our refresh cycle, we are deploying servers based on the 4th Gen Intel Xeon Scalable processor family in our data centers. By doing so, we have significantly increased EDA throughput performance to improve the overall EDA design cycles and optimize time to market of Intel® chips.

After our successful, proof-of-concept (POC) testing of our on-premise platform, where we found that the Granulate software reduced 25% of memory and 44% of CPU utilization, while offering potential cost savings, we began testing in the cloud Databricks data lake. Collaborating with our technology partner Accenture, Intel IT performed a four-week POC with the active Granulate agent in our Production and Quality Assurance (QA) environment running on the Databricks platform securely in the public cloud. The results included a 23% improvement in vCore compute consumption, a 17% increase in data throughput, and reduced CO2 emissions, aligning with the on-premises benefits we observed previously for another big data workload.

In this Tech Barometer podcast segment, CIO of Delaware Valley Community Health, Isaiah Nathaniel describes his spiritual and purposeful approach to helping the healthcare provider leverage all the innovation IT can bring.

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

Transcript:

Isaiah Nathaniel: Never let anyone beat you giving. Never let anyone beat you loving. Never let anyone beat you at believing. And finally, never let anyone beat you serving.

Jason Lopez: That’s obviously a spiritual insight. But Isaiah Nathaniel has a double meaning here. It’s also about the role of IT.

Isaiah Nathaniel: You’re hitting a nerve. I come from a long line of preachers as I told you my first time, but here we are.

I am an on-prem guy. 

Jason Lopez: Isaiah Nathaniel is a CIO. He works for, or as he would mean it, serves Delaware Valley Community Health, a hospital that offers medical, dental and behavioral health services in nine locations around Philadelphia. They’re noted for focusing on accessibility and affordability, regardless of a patient’s ability to pay. And that’s something which informs Nathaniel’s work leading his IT organization. He may be an on-prem guy.

Isaiah Nathaniel: But there is flexibility in the cloud if you do it the proper way. So for us, it’s really being able to be flexible to scale at the drop of a dime based upon the business vision or the business need, but then also scale back. 

[Related: Why Validating Medical AI is Crucial to Improving Healthcare Outcomes]

Jason Lopez: So he says if you can afford it, you can throw things into a public cloud. But his experience has shown him, it’s not fiscally responsible to operate with a blank check.

Isaiah Nathaniel: Any fiscally responsible CIO would say that’s not the way that we want to conduct our hierarchy of our business unit. And so for me, it’s okay, let’s dip my toe in the water, but let’s also make sure it’s fiscally responsible because waste is something that I don’t like because waste takes away from patient care. It takes away from staff retention. It takes away from the ability to grow. And particularly in the non-profit space, you’re looking at that bottom line to be able to say any extra spend really does take away from truly good quality patient care, patient experience, and then staff retention, relationship, and ability to serve who we need to serve.

Isaiah Nathaniel: Nutanix saved my organization by having that infrastructure.

Jason Lopez: The pandemic forced some profound changes in the way many businesses run. Spectator sports and movie theaters basically had to shut down. Restaurants had to drop everything and shift to takeout. And it seems everyone else met online via video. And in that idea, just think of how the demand for digital services affected IT. There was an accelerated shift to remote work, forcing IT departments to quickly adapt their infrastructure. Companies on a 5 year digital transformation plan suddenly recalibrated to do it in a year or two, and budgeting went back to the drawing board. Cybersecurity and disaster recovery efforts got amped up, and automation rollouts got pushed forward. Vendors like Nutanix were key, Nathaniel says, because he had to rely more on cloud services, needing more IT agility and scalability in the Delaware Valley Community Health IT environment.

Isaiah Nathaniel: Before, we couldn’t control a lot of the things that were happening. You just had this sense of angst because you did not know what was coming next.

[Related: Alex Karargyris’s Path to Becoming a Pioneer of Medical AI]

Jason Lopez: But in looking back at the years of the pandemic, he says a lot of things he and his team launched worked and much has stayed.

Isaiah Nathaniel: Now we can say, all right, we know where we are. We know where everything can possibly be, not only from just a personnel perspective but also from an infrastructure perspective. So we can say, hey, I know where this workload is. I know if I need to migrate this workload, I can do the same thing with one simple click. But then also from a security perspective, using products like Nutanix Flow that we instituted after the pandemic started and saying, “hey, now that we’re stable, let’s get more stable. Let’s take our scalability and be able to deliver workloads where we want them to be, but also secure them so that they aren’t impacting other workloads that need the productivity that we needed to have.”

Jason Lopez: There’s a balancing act he needs to do, between on-prem and the cloud, hands on and automation, and maintaining flexibility… which he refers to as enabling.

Isaiah Nathaniel: Enabling means actually not being the no people, saying, here’s a great idea. Give me a good use case. Make sure it’s secure. Bring it in the environment. We have the scalability because of our infrastructure to be able to test things out from a development perspective. And if it goes to market, so be it. And then we’ll go back to the whole wheel of innovation from there. I call it the scalability because I can pick my flavor of the day or flavor of the week because there’s a new app popping up literally every minute. And it’s so fun because I like the development that’s happening. I like the ingenuity. I like the innovation that’s coming out of these things. It’s about where’s the business case? Where’s my business need? And then we’ll throw an application potentially into it or add to one that already exists.

[Related: University-based Medical System Innovates Healthcare with Hybrid Cloud IT]

Jason Lopez: We’ve been talking a lot about healthcare IT here on Tech Barometer over the past year and privacy and security just can’t be emphasized enough. If you ever find yourself working in healthcare IT, it’s going to be ever present at the top of your to-do list. 

Isaiah Nathaniel: When we look at legislation like the 21st Century Cures Act and how that impacts data at the point of care for the patients being really truly in charge of their information, my data is no longer four walls sustainable.  It actually is beyond my four walls. And so for me, it’s make sure that, one, at rest it’s properly secured. But then in transmission, it’s extra secure so that when the patient gets it or the referring hospital and or specialty gets it, they can also say at that point it was secure. Your infrastructure internal to that, under that firewall, under that brick, is where the special sauce is. But I can at least say,  CIO to CIO, “hey, you can trust my data.”

Jason Lopez: We started this report with Isaiah Nathaniel recounting a spiritual guide. It’s how he thinks about his role as the CIO of Delaware Valley Community Health. But it’s a lesson he learned from his uncle, Reverend Herbert Lusk II, who died in 2022.  In the 1970s, Lusk was a professional football player who played for the Philadelphia Eagles under coach Dick Vermeil.

Isaiah Nathaniel: He was part of the team that went to the Super Bowl against the Raiders. He told Dick Vermeil, I’m only going to play three years, and then I’m going to go into the ministry. I’ve got a calling on my life. He was getting ready to be the lead running back, brand new contract, and he followed his calling. And that calling took him to North Philadelphia. North Philadelphia was depleted in the 80s. It was one of the worst urban areas in the country. And he took a church that had less than $5 in the bank account and over $500,000 in structural damage. Forty two years later, he rebuilt the church, over 1,000 members, welcomed three presidents, built the first welfare to work program in the country, and he left a lasting legacy. And to make this even more poignant, where the church is, is actually across the street from my home office. So his words to all of us as his children, his nephews, nieces, were these four principles. Never let anyone beat you giving. Never let anyone beat you loving. Never let anyone beat you believing. And finally, never let anyone beat you serving. When you talk about moments like that, when you lose such a powerful figure, I get up every day with those four principles. And my job is to serve my patients through access of technology and give them what they deserve.

Jason Lopez: Isaiah Nathaniel is the Vice President and Chief Information Officer for Delaware Valley Community Health in Philadelphia. This is the Tech Barometer podcast and we’re delighted to bring you these profiles of people in tech who are doing remarkable things. If you like what we do here, check out more stories both podcast and text pieces at the theforecastbynutanix.com. I’m Jason Lopez, thank you for listening. 

Fertility difficulties affect 1 in 6 couples globally. What comes as a surprise to many is that nearly half of these cases are related to male factors. However, due to several barriers including social attitudes and misconceptions, many men do not seek reproductive health care or even know there may be an issue. In recent years, we’ve seen increased awareness of topics related to women’s reproductive health, begging the question: will we see a similar boom for men’s reproductive health?

On this episode of the Business Group on Health podcast, we speak with Dr. Joseph Alukal, a leading clinician and researcher. Dr. Alukal sets the stage for how we can open the dialogue about men’s health through better understanding when and where to seek reproductive care.

Guest: Joseph Alukal, MD | Associate Professor of Urology at Columbia University Medical Center and Director of Men’s Health at Columbia New York Presbyterian

Thank you to today’s episode sponsor Progyny.

In this Tech Barometer podcast, Nutanix President and CEO Rajiv Ramaswami explains the challenges his hybrid multicloud customers are facing as they move their organizations into a future increasingly powered by artificial intelligence.

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

Transcript:

Rajiv Ramaswami: I am optimistic about technology and its role in the economy because regardless of where I think the economy is, I think companies are going to invest in technology. They’re going to invest in software. If you look at the rates, I mean whatever GDP growth, I think tech spending will be growing faster than that. And then software spending within tech is probably going to grow faster than that just because of the nature of how companies are going digital. It’s a business imperative. Companies struggle with getting good software talent. There isn’t enough to go around. AI talent is even harder to get. The set of tools that you need to put together AI applications and get them going to market, that’s not easy either. And then finally, on top of that, there is a shortage of hardware – GPUs – today in the market. Even if you want to do it, you’ve got everything to do it, you can’t get a hold of the hardware.

Jason Lopez: You might be wondering, didn’t the speaker start by saying he’s optimistic about technology? This is the Tech Barometer podcast. I’m Jason Lopez. What follows are comments from Rajiv Ramaswami, CEO of Nutanix. These come from a conversation captured by an audio recorder in the room. So we thought it would be interesting to whittle this down into a short podcast to hear him essentially riffing on the topic of AI in the enterprise, which has become essential for companies trying to empower workers or streamline or automate.

Rajiv Ramaswami: Whether there is automation of business processes, whether it’s customer support, whether it’s document analysis and search or processing credit card applications or market applications or what may be, or making software developers more productive through co-piloting or enabling more automated capabilities like better fraud detection, et cetera. So these are all big things that can move the needle in a very significant way.

[Related: 9 Predictions for IT in Age of AI]

Jason Lopez: Rajiv sees momentum building, at least on the development side, in the next year or two. There’s an expectation for the emergence of the first wave of generative AI applications, building on the ecosystem. But the challenge will be to overcome a few things like GPU storage, cost benefit analysis and getting higher quality results.

Rajiv Ramaswami: If you’re, for example, using some sort of a copilot to generate snippets of code automated, for example, how good is the code? Can you trust it? Can you go send it out? Whatever use case may a customer service use case or where you’re looking for document search and retrieval. So getting the accuracy to a reasonable level where you feel comfortable. That’s another thing that you have to do and you have to keep training until you get to that level before you can actually put it into production and do inferencing.

Jason Lopez: AI implementation isn’t inexpensive. The cost of compute clusters for training can be very high, and Rajiv sees companies having to really clarify the ROI and make a business case for AI adoption.

Rajiv Ramaswami: And so I think people are also going to go through that life cycle of, okay, there’s a lot of interest in AI, and then they’re going to go implement and they’re going to say it’s expensive to implement and therefore they’re going to be, again, looking at business cases like everything else. I think we’ll go through that phase as we move forward here with AI, I think there’s a lot of potential and to realize the potential, you also have to pay attention to what it’s going to take to get it done and how much it’s going to cost and make sure you’re getting a benefit. And then you’ve got to go get it done. You have to go implement it, which means putting together the team of people, figuring out how to get your data in order, how to choose the right large language model, how to train it properly, how to reduce the size of the model to what you need, and then of course, train it and get into production, get the fidelity of the data results and then put it into production.

[Related: Generative AI Propels IT Modernization]

Jason Lopez: And this presumes you have the talent on board to make it happen.

Rajiv Ramaswami: It takes data scientists, it takes AI engineers and machine learning operational engineers, and then it takes good infrastructure people along with the developers who build the app. So it takes all of these skill sets to go bring this to life.

Jason Lopez: The other challenges of AI implementation revolve mainly around data management. And as we’ve already heard, it requires high fidelity data in the right place for effective algorithm functioning.

Rajiv Ramaswami: Getting the data together in the right place itself is a massive task for many customers. And the second is you have to run the AI algorithms where your data is because data has gravity and you need to protect that data. You don’t want to give up your IP when you run a general purpose AI LLM, for example, on your data. So you have to protect that as well. So that’s the other set of considerations that emerge when people are running these AI applications. And then we hear about large language models that are in the trillions of parameters and they can do great things, but they’re also very expensive. And so the question you have to understand is, what is it that I need from my application? Do I need that big a model? Can it do a smaller model? And I think there’s a lot of optimization that has to happen there also.

[Related: Developing in the Age of AI and Multicloud]

Jason Lopez: Nutanix has gone through a process of AI implementation and uses a cloud platform known as GPT in a box. It provides an integrated solution with storage and machine learning toolkits.

Rajiv Ramaswami: So we can help our customers run their AI applications using a platform that’s close to being a turnkey platform, and it can be used wherever their data is sent. And so this is a platform that we call GPT-in-a-Box. It’s our usual cloud platform. It’s what we all use it for, running every other application. In this case, we also include files and object storage with our unified storage because all these applications need a lot of storage for the data. And then we include some commonly needed machine learning and operational toolkits as part of this model, because again, customers don’t have the where with all to go integrate everything that’s needed to run an AI application. So we try to integrate everything that’s under the covers needed to run an AI application so the customers can focus on the application itself, and they can rely on our platform to go run it wherever they’d like to run, wherever the data is present.

[Related: Generative AI Moves Beyond Hype for IT Operations]

Jason Lopez: Twenty-twenty-three was the year of large language models.

Rajiv Ramaswami: Twenty-four, I think, is going to be the year where it becomes more real. People start building applications, especially not consumer applications like chat and writing poems to your friends using ChatGPT, but real enterprise business applications that could be running. So this is probably going to be the year where people start using these for good business use cases. I think that’s the case there.

Jason Lopez: Rajiv Ramaswami is the CEO of Nutanix. This is the Tech Barometer podcast. I’m Jason Lopez. Check out another Tech Barometer podcast featuring Rajiv’s comments on the Nutanix roadmap in the story why hybrid multi-cloud matters. You can find it and other tech stories at theforecastbynutanix.com. Tech Barometer is a production of The Forecast. Thanks for listening.

Despite the continued buzz about GLP-1s, obesity treatment does not end at the pharmacy counter. On this episode of the Business Group on Health podcast, we’re joined by Dr. Scott Kahan, a practicing obesity medicine specialist who leads the National Center for Weight and Wellness, a multidisciplinary obesity treatment center in Washington, DC, to discuss evidence-based care.

Dr. Kahan speaks about the current state of obesity treatment, the progress made in the nearly two decades that he’s been practicing and the impact of societal dialogue surrounding GLP-1s. By 2030, 50% of United States adults are projected to have obesity — listen to this episode to learn how the treatment landscape may need to transform to accommodate the growing need.

Guest: Scott Kahan, MD, Director of the National Center for Weight and Wellness

Thank you to today’s episode sponsor Progyny.

In this Tech Barometer podcast segment, Nutanix President and CEO Rajiv Ramaswami discusses the challenges driving more IT leaders to develop hybrid multicloud strategies for managing across private data centers, public cloud services, edge computing sites while enabling new application development and AI capabilities.

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

Transcript:

Rajiv Ramaswami: As customers here, all of you expect to have solutions that meet your business outcomes. We solve some portions of that, but we don’t claim to solve everything. So clearly that’s why we are investing in the ecosystem here around us, the ecosystem at every level, the ecosystem in terms of our OEM partners, our cloud partnerships, our best-of-breed partners in areas that we don’t play in like security for example, in cloud native Kubernetes and public cloud providers. We all realize that we operate in this broad ecosystem and we have to come together to help you get that solution. So that’s what we are committed to doing, we’ll continue down that path and we hope to create deeper and deeper relationships with our ecosystem.

Jason Lopez: What you’re hearing is a talk Rajiv Ramaswami, CEO of Nutanix, gave to Nutanix customers at the Nutanix dot NEXT conference in 2023. He highlighted several partners, such as OEMs like HP, Dell, Lenovo, and Super Micro, a security partnership with Palo Alto Networks, cloud-native services with Red Hat, collaboration with major cloud providers like Azure and AWS, and with Citrix, particularly in VDI workloads which represents about 20% of Nutanix’s platform usage.  This is the Tech Barometer podcast, I’m Jason Lopez. In this talk, Rajiv addresses hybrid multicloud, public cloud, edge technology, platform services, and a brief picture of the Nutanix roadmap. But to get back into the talk, here Rajiv touches on the company’s support of AI application development and its integration into services. 

Rajiv Ramaswami: There are two parts to that role. The first part is many of you are developing new applications that do use AI, and we want to be the platform that you can run those applications on. We are also committed to open source on this. There’s a lot of open source work being done on AI. We are also working closely with Nvidia because a lot of these use GPUs and we expose DPU through our platform and help you use them in an efficient way. Clearly one whole approach around making these AI workloads work well on our platform. And some of these tend to be very industry-specific workloads like retail and manufacturing as examples that I mentioned. So that’s part one. Part two is using AI internally within our company’s offerings in terms of the products and services that you consume from us. So in fact, our Prism Pro, by the way, the R & D team for Prism Pro is actually called AI Ops because we truly believe that we can actually make operations more efficient by using machine learning and deep learning. That’s just one example of where we are starting to use some ai. Another example is we all, at least a good chunk of you actually provide telemetry data for us. We’d like to do more with the telemetry data. Of course, protecting identities, protecting privacy, of course, is very important there. But being able to look at the telemetry data, we could potentially be doing things like predictive analytics and helping you understand what needs to be changed. And so there are potentially new use cases there. And then of course when it comes to support, I want to make sure we retain our NPS there and we continue to do a great job supporting you, but there’s also AI that can be used inside to help make that process even more efficient. 

Jason Lopez: Now imagine, if you will, a digital landscape, with applications and data freely roaming across multiple domains. In the middle of this, there’s the home base, the on-prem setup. There’s the edge, on the outskirts. Then there are the public clouds. In this next part of Rajiv’s talk, he hones in on creating a single, unified platform that bridges these diverse places..

Rajiv Ramaswami: The reality is yes, most of you are actually hybrid multi-cloud in the sense that you have these applications and data in multiple places. You have your on-prem, you might have your edge, you have public cloud, a, public cloud B for the most part. Each of these is different. They operate as silos. You have a different stack, you have different processes, tooling, and security to manage each of these. That’s actually what hybrid multi-cloud is today. And what we aim to do is to simplify that for you with a single platform at the infrastructure level that cuts across all of these and gives you that same experience, same tools, same processes, and complete flexibility to run your applications wherever you’d like across these environments and manage them all with a single team. That’s really, I think what we are aspiring to do, and we’ve got a lot of proof points along the way. We’ve got our offerings today on AWS on Azure with a number of managed service providers, and we are seeing an increasing number of edge opportunities in very specific verticals. Manufacturing, and retail, for example, just to name a few. And of course, defense. 

Jason Lopez: Attitudes toward using public cloud services are changing. Nutanix initially embraced the cloud for certain services but eventually had to reconsider and move some of their operations back to a private data center to control costs more effectively. Here, Rajiv addresses this.

Rajiv Ramaswami: We are certainly seeing people being a lot more careful about what to do in the public cloud or what not to do because if you had asked this question three, four years ago, a lot of people in the room would just say, I’m going to the public cloud unless I’m a dark site. I’ve got some specific requirements. A lot of people would say, I can go to the public cloud. And then a couple of things happened over the last few years for those of you who have gone, I think you’ve realized that it isn’t as easy to take everything you want to the public cloud. It can be very painful for lots of applications and will need refactoring and potentially re-platforming. So not easy to take your existing apps. And for those new apps that you’re building, it’s great. It’s an easy on-ramp. But once you start running it at scale, and once you start running it continuously, the cost starts Up. And we’ve experienced that even at Nutanix. My cloud bill keeps going up every year and we keep trying to look at what we can do to optimize this. And I’ll give you one example of what we have done, on a much smaller scale. One, during COVID, we started a program called test drive. Test Drive ran on Google Cloud. Actually, it’s a nested hypervisor for those of you who want to be technical, but they didn’t have a bare metal offering at that time. And it proved to be quite popular. We didn’t charge for it because it was just an online proof of concept. And the use has continued to grow like crazy for us. And our cloud bill, therefore from Google, keeps going up every year. We said, okay, well we’ve got to now it’s starting to get serious. This is here with us to stay. We have to treat this properly and figure out how to optimize it. And so what did we do? Well, we moved back a chunk of what we call the steady state workloads back to our data center. We run that very cost-efficiently. Then, for burst capacity, we use the cloud, and it’s working out well. It’s certainly saving us some dollars when it comes to cloud costs. And what you’re going to see is a lot of companies realizing going through the same journey that we did, which is there’s an easy on-ramp to the public cloud. It’s an easy way for you to get a new app out there quickly, and if you don’t do it right, you’re going to be locked into the public cloud. At least in our case, it was portable. We could move it. It’s tempting to go use all the services, get there quickly, and build this app and run it. But then you realize that as you start scaling, at the end of the day, they have to make their margin. So you end up spending more and you can certainly, I always say a well-run private cloud infrastructure can certainly beat the public cloud in terms of pure cost. There’s no doubt about that. And I think lots of you probably have experience with that and you’ve done that work already. So we are certainly seeing that as, I don’t know if it’s maybe a little too early to call a trend, but I think what certainly we’re seeing as a trend is people being much more circumspect about what’s going to go in the public cloud. And I can’t say I’ve seen a trend in terms of everybody moving back from the public cloud. 

Jason Lopez: Edge means different things to different people. But Rajiv points out its evolution has paved the way for a new generation of edge applications. New use cases demand advanced computing power, containerized applications and centralized automated management systems.

Rajiv Ramaswami: The way I would think of edges would be, let’s call it reasonably high compute edges. Some people call it near edges. And then other people say, I call it far edges where you have much lower cost, very simple solutions that are more OT type deployments. So for us, we are more focused on the compute edges. In fact, to put this in perspective, what you are deploying as a server in the data center three years ago, you can actually get more computing in a single server sitting in the edge today than you probably got in your data center a few years ago. So imagine tons and tons of compute potential available in the edge by deploying very few numbers of servers and a small number of nodes, one-node deployments, and three-node deployments. And so there’s a lot you can do with it. But more importantly for many of you, there’s a whole new set of applications that are emerging at the edge. If you’re in retail, we are talking to people about fraud detection. We are talking to people about automatic checkout manufacturing. We talked to car manufacturers who are saying, I want to have completely automated visual inspection of defects through machine learning and ai. So just to name a few here. And so there’s just a range of these new edge applications coming in. There’s a lot of data being generated at the edge as well, and you might be able to do training for some of these AI applications in the cloud, but inferencing will likely be done locally. And as you look at this, these edge use cases actually starting to get fairly compute-heavy, and we are engaged with many customers today in terms of helping our solution optimize for those specific use cases. Now, what’s common in addition to meeting these specific application needs is that most of the new applications are containerized. There’s a lot of AI being used and data management being needed, and there’s also a need for clearly centralized management, provisioning, upgrades, everything because you’re not going to be able to go out there and have people manage your edges. It has to be automated. So that’s what we are seeing today, and I’m sure this is going to continue to evolve over the next few years. 

Jason Lopez: Rajiv’s talk moved on to a vision for simplifying the management of applications across platforms. He says it comes down to a platform service vision.

Rajiv Ramaswami: If you look at applications today, modern applications are being built with containers and Kubernetes, and that Kubernetes substrate is available for you everywhere. I would almost go so far as to say it’s starting to be commoditized. It’s everywhere. You can get it on-prem, you can get it in AWS, they have EKS, and you can get it in Azure. They have AKS. You can get it everywhere. Google has GKE. So that the compute substrate is available everywhere. In addition to the compute substrate, all apps also need a set of data services. Most apps, almost all apps will need databases. They’ll need messaging and streaming. They’ll need caching, they’ll need search. And this is a set of services, most of them related to data that’s available today in the public clouds. The only issue there are the public clouds, so that they tend to be siloed. It’s not easy for you to go from one to the other. It’s not easy for you to have flexibility in terms of avoiding locking in. And so what we are looking to do is to provide you with a consistent set of these data services across everywhere, right? Across all native substrates. You can run it on top of a Nutanix infrastructure, of course, wherever that’s available. But we are also going to enable these services to be available natively on AWS, Azure, and other native cloud substrates, so that you can truly then think about building an app once using this set of services. You could see how easy it is to be able to deploy that app anywhere and also not be locked in.

Jason Lopez: In this last section of Rajiv’s chat with customers, he touches on the company’s plan for enhancing their data services, especially in the context of Kubernetes and containerized applications.

Rajiv Ramaswami: So really you’ll see a range of data services for Kubernetes and containers that will essentially help you to run these modern applications and deal with containers much the same way as you deal with a VM. We always do snapshots, but now we are going to be doing snapshots and being able to store the snapshots in low-cost public cloud object stores or any SC-compatible object store for that matter that has a lot of use cases and implications. They’re going to provide you with a single console for you to look at your entire estate and manage your entire estate across on-prem public clouds and edges. And the fourth is more of this foundational vision around our future, how we can help you build these applications, modern applications in a portable way, using a consistent set of data services and being able to run them anywhere. This vision is for us to help you build portable applications. This is a five, 10 year journey for us and we are just getting started with our Nutanix database service. So I’m excited about the future of the company. I’m excited about how we can continue to meet your needs, not just for today, but also as you go forward.

Jason Lopez: Rajiv Ramaswami is the CEO of Nutanix. This was a talk he gave before customers at the Nutanix dot NEXT conference in Chicago in 2023. In May of 2024 the conference moves to Barcelona, Spain. This is the Tech Barometer podcast, I’m Jason Lopez. Tech Barometer is a production of the forecast. If you like what we do here check out more stories at theforecastbynutanix.com.

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On this episode of the Business Group on Health podcast, we speak with Dr. Galiatsatos, a critical care physician serving on the Johns Hopkins Post-Acute COVID-19 Team, about his experience treating Long COVID patients, the current status of research and how employers can best support employees at risk of developing Long COVID.

Guest: Panagis Galiatsatos, MD, Associate Professor of Medicine, Division of Pulmonary & Critical Care Medicine, Johns Hopkins School of Medicine