 All right, I think we're going to go ahead and get started for a couple minutes into the hour. Looks like we have a good group of folks here. So I'd like to thank everyone joining us. Welcome to today's CNCF webinar, which is a bit of a mouthful, but Automation AI and ML for Network Management. It's already here. Our organization's ready. So the good news is I'm just the moderator. Because I don't know much at all about these topics, but hopefully like you, you're here to learn something, something new. My name is actually Phil Estes. I'm a Distinguished Engineer and CTO for Container and Linux Strategy at IBM. I'm also a cloud native ambassador, and I'm moderating today's webinar. And so I'd really like to welcome our presenter, Andy Singer, who's the VP of Marketing at Kentick. Before I turn it over to Andy, just a few housekeeping items. Obviously, as a webinar setup, you're not able to talk as an attendee. We'll hear from Andy, obviously. But if you have questions, we'd love for you to use the Q&A box at the bottom of your screen. Obviously, if you've been on Zoom before, it's easy to use chat, but the Q&A box would be a great spot. Taylor from the CNCF will probably point you there if you post a question in chat. But that way we can actually track questions and make sure they get answered. So feel free to drop questions in there. This is an official webinar of the CNCF. And as such, it's subject to the CNCF Code of Conduct. Please don't add anything to the chat or the Q&A that would be a violation of that code. Obviously, what we mean there is please be respectful of all your fellow participants and the presenter today. So with all that said, I'd love to hand it over to Andy to kick off today's presentation. Thank you, Phil. Just making sure you guys can hear me okay. I think so. Sounds good. All right. Good morning, good afternoon, and good evening to everyone. And thank you for joining today's webinar. Phil, I'm, you know, Kentick is honored to be able to present to this audience and we appreciate you moderating today's sessions or session. If anybody has questions during today's presentation, feel free to ask. Phil will moderate and I will do my best to answer them. And if I can't, we will get you a response after the webinar. So today's topic is automation, AI and machine learning for network management. It's already here. Our organization's ready. On the agenda for today, I'm going to quickly tell you about Kentick, who we are and what we do. We'll talk about why automation is important, kind of a state of the market overview. Then we'll go into some findings from a survey that we released earlier this year, specifically talking about artificial intelligence, machine learning, cloud adoption, and some interesting trends. We'll talk about AI ops, which is what Kentick is talking about these days, and go over a summary of our findings and some recommendations for everybody. And then we'll do Q&A. So first, a little bit about me and Kentick. So I lead the marketing function here at Kentick. Kentick provides revolutionary insights and automation to make every network excellent. We power up operations teams with AI ops for network professionals. We were founded in 2014. Our headquarters based in San Francisco, and we have employees all over the world. And we have over 250 customers. So first, I want to spend a few minutes talking about why automation is important. And I'm sure a lot of you deal with automation every day, but I thought it'd be interesting to give you Kentick's perspective on how we see the industry moving, what's happening, and what are some of the challenges that network professionals are facing today, and why that's relevant for cloud. So many of you already know that digital businesses drive the fastest revenue growth in history. And you can take a look at companies that have come to market today, and just over the last 10 years, they've had explosive growth. And a big contributor to all that success are the networks that underpin all of those businesses. Today, it's very much a cloud centric world. And I think we all know that just by nature that we're on a CNCF webinar. But it's an important thing to call out that the cloud is really at the center of everything that happens today with digital businesses and most businesses in general and for consumers. We know that all this data is flowing to and from different clouds to and from different cloud based applications. Many enterprises and businesses and providers have lots of data centers, whether new or software defined or legacy data centers. In addition, companies and enterprises and organizations still have and rely on headquarters and branch offices. All that connectivity flows to and through the internet and to and through and from the cloud. We know that networks are changing. And in my experience in the last, you know, 20, 25 years, I've seen tremendous change in networks. But I think some of the most transformative changes come in the last 10 years that we've seen the rise of the cloud and what that's done for networking. So now today we have multiple architectures in place, physical, virtual. We've got increased traffic complexity. We've got a ton of API driven use cases. So the rise of API interconnectivity is really driving a lot of the explosive growth that's taking to place today, both in networks and in applications. We've got an increasing amount of automation and we're going to talk about that. But when we talk about automation, we're specifically talking about manual tasks that can be automated to drive increase efficiency and efficacy. We've also got varied security threats. So with the rise of cloud, we've seen a whole new wave of security threats, everything from endpoint based attacks to network based DDoS attacks. We know in all of our conversations with our customers and prospects and the market that we engage that network teams, network professionals, all up and down the market, small companies, medium companies, large companies are struggling to some degree. We know that network outages are becoming a problem, largely due to the complexity that's there today with networks. A recent Amazon outage was estimated to cost about $99 million in lost revenue. Southwest Airlines network outage costs had the potential to reach approximately $82 million. And when we think about and when we talk to the people that are thinking about outages every day, here are some of the things that bubble up. Here's what they tell us. They say, number one, we need to be able to quickly detect problems. And we know it's difficult because of the complexity of the networks that we have today, whether we're 100% cloud, multi cloud, or even hybrid cloud, there's a huge need to be able to quickly detect problems. We know that performing root cause analysis is even more complicated, just by nature of the fact that there's so many different types of data coming in that you need to be able to analyze and need to be able to look back at data, historically to be able to really understand the root cause of a network problem. Migrating applications is becoming increasingly complicated, not just because it's hard to begin with, but because of things like different architectures, whether you're using containers or not, whether you have single cloud or multi cloud, multiple operating systems in place, even multiple hypervisors and virtualization platforms, migrating applications is a big challenge. Planning for capacities, another one. Capacity is really important because of the demand out there for applications in the cloud. You need to be able to understand, do you have enough capacity, where are you going to need new capacity, where are you exceeding your plan capacity, and are you paying too much for that capacity. And then that leads me to allocating network costs. More and more, as different types of transit are out there on different types of networks are in place, network professionals have the ability to play a bigger role in helping businesses maintain and manage the costs for network. So I want to now transition into a survey that we conducted called the state of automation, artificial intelligence and machine learning and network management. So we went to Cisco live earlier this year, we executed our survey and we got 388 conference attendees to give us really valuable data on everything from their business and industry to their thoughts on several key questions around this specific topic. The respondents were had lots of different titles we got C level folks SVPs VPs, but a majority of respondents about 50% reported having a title of network engineer. So the biggest group of respondents to our survey were actually the people who were working on the network hands on every day. So here's a brief summary of our findings. So the move to cloud is still underway for a few, but multi cloud is the reality for many. And I think that's big, even just one or two years ago, hybrid cloud or multi cloud wasn't as popular as it is today. Network automation is taking shape. And we'll provide some data that talks about how the types of automation that are being used goes across many different categories and even taking place more and more on network management tasks. The third finding is networking processes like compliance and incident response are less likely to be automated. So when it comes to things that have to do with regulatory issues or even security, those tasks are still being performed today by people and not being automated. And finally, machine learning is growing in importance for network management. More and more people are realizing that the complexity of today's networks and the speed at which we operate and the real time nature of all of our businesses requires more and more machine learning to be used to be able to help everybody better manage the networks and we this was true across the board industry title regardless of who you ask. Phil, I'm going to pause right there just for a quick second. Do we have any questions in the Q&A. We don't yet at this point. Okay. All right. So let's jump into some of the findings. So as the move to cloud continues multi cloud looms large. So our analysis found that 53% of respondents are using a single cloud provider, but nearly half are using multiple cloud providers. Some additional data are includes for example, the greatest adopters of cloud services are the education sector. The last majority of respondents said that 50% or more use only one cloud technology and finance sectors gave the strongest responses confirming multi cloud use at 43% and 38% and the energy sector was a close third at 37%. In addition, you know, we think about these results. Well, our takeaway is that networking pros are already dealing with significant operational complexity. It's hard enough to move from a physical to a virtualized environment. It's even harder to move networking into a cloud providers environment, and it's even more complicated to work with multiple cloud environments. Hey Phil, I see a question here that we've got do we want to see if we want to take it real quick. Yeah, absolutely. So, Josh asks, do you have any stats on how much of the industry invite industry move to cloud, either completely or partially. So, I think from our report, we've got a couple different stats we've got some things by vertical showing that how they moved to the cloud and actually a great way to get you this information is to actually get you the report, which has actually cloud and multi cloud by industry. So what we'll do is we'll put the link in the q&a to be able to download the report and that will better answer your question. All right, great. Thanks. So our second key finding is around network automation. It's definitely taking shape. So what we asked our that what we asked respondents is how ready is your organization for full automation to manage network performance and network security. Our takeaway is that 85% of respondents reported having at least one network automation deployment in place. 55% reported having two or more deployments and 27% of respondents reported that their organization is either extremely prepared or very prepared for full automation. Now, this in my view is actually really exciting because even as long as just last year or even two years ago, network automation was kind of largely shunned as something that's maybe frightening or unreliable. But that's changed the more and more companies are adopting software defined networking automation takes a bigger and bigger role. But I see broader IT organizations in general are no longer willing to wait for networks to be manually configured. So when it comes to cloud, we know that things like provisioning, bursting, these are all things that happen automatically. And a lot of the networking in there is automatically provisioned. But when it comes to network management, troubleshooting and remediation, these are the things where automation is taking the next big leap. You know, when we look back at last year, we did the same survey and same question and last year survey, and we show that only 15% of respondents felt their organization was prepared for automation. So we've had a huge increase in the number of companies saying, yeah, we're ready for automation. When we look at the specific industry results we got, the energy sector is really ahead in terms of adopting automation. Obviously technology is big, but energy was up at 30%. And they're very prepared or fully prepared for full automation. We think about why the energy sector is leading. You know, we think that their industry is more hands off in many network management processes. Just because of the distributed nature of their environments. And they're probably more worried about managing and real time production issues. So when we dive deeper into automation, when we ask the respondents kind of what are they using automation for? These are the top categories, cloud bursting, configuration, compliance, incident response, policy management and workload management. A majority of respondents 53% are deploying automation for configuration purposes. And that makes total sense. There's a ton of manual effort required in doing configuration around networking. And it makes sense to automate those tasks. 40% reported using automation for policy management. And this is actually a very interesting step. More and more automation is being used for policy configuration. So whether you're getting your creating policy on a daily basis or you're modifying policy in real time. Let's say you're moving a network interface from A to B, or you're configuring different routes, all those policy changes are becoming increasingly automated. It's also worth noting that despite the rise of hybrid and multi cloud environments, cloud bursting is being automated the least for organizations. What we think about this is that the whole point of cloud bursting is to do so on an automated and dynamic basis. Our thought is that the networking components to enable cloud bursting are most likely already in place and don't need to change. So cloud bursting is a special enough use case that's not common amongst all these respondents. Now, there's two ways to look at it. One way people could just say it's already automated in my virtualization platform. Therefore, I don't need to worry about it. I don't think it's truly automated. It's kind of built in. The other way to look at it is to say, yeah, we're actually using additional automation. I think it's a mix of both. Then when we get into machine learning, we know that machine learning is growing in importance for network management. So when we take a look and we asked everybody their stance on machine learning and overwhelming 65% of respondents said that machine learning is now extremely or very important for network management. That represents a 20% year over year change. We asked that same question last year was only at about 40% or so. It's a huge increase and our take in our analysis of the data and based on conversations with lots of people in the network industry is that this increase in adoption of machine learning is really driven by the pain experienced in trying to apply and scale manual processes. So I see another question coming in Phil. Let's see if you want to take it right now. Yeah, sure. So it's a little bit long. I'll read it. What are the major concerns? Is that a simple mistake in a single network configuration? For example, default route or something could bring down an entire macro network. What kind of tools or mechanisms can we adopt to make it foolproof? Well, maybe I'll stop there and see if you want to save that one or take it now. Yeah, I think that's a great point. I mean, you know, there are lots of stories out there where, you know, quote unquote fat fingering a script can really cause a lot of outages. I mean, we've seen horror stories of VM where administrators in kind of accidentally knocking out thousands of VMs through the push of a configuration script. Same thing can happen in network configuration, especially when it comes to route information. So I think, you know, you asked, is there a tool or a mechanism? You know, I would say personally, things like having two levels of approval to implement changes are making sure that you have someone check your work before you issue it. Those kind of processes are always good to have, whether they're manual or automated. I also think that there are some things that, you know, when you're talking about scale or operating a production, maybe they shouldn't be automated. So automation really needs to make sense for your organization. So, and how you apply it depends on your level of risk. Do you want to take the second part of the question, Phil? Yeah, yeah, so there's a there's a discussion about, you know, we do a lot of manual verification of a config today. Before CI, we actually have a test suite, you know, pass fail before you push a change. Is there something similar we can do in the network world before pushed to production? Yeah, I think that's actually another great question. So being able to kind of forecast what your configuration changes are going to look like before you actually implement them. I think that's a great way to approach it. Well, like I said before, I think it's also good to have more than one person be able to validate configuration changes. And again, I think it also depends on your environment. So I think using a test suite, or, you know, in your platform being able to kind of forecast changes or even pause changes before you implement them, just to get them validated is a great step. Okay. I want to jump in quickly now and talk about AI ops. So we talked before about how machine learning adoption is really increasing across the board, especially when it comes to folks working in cloud. The reason for the increase really comes from the increase in number of manual processes that are out there and the complexity associated with them. So having a platform that can help you make sense of all the information is really critical. And this is how we get into AI ops. So first, a quick definition. AI ops is a category and a term created by Gardner. It first came out in 2017. And in last year in November, they produced something called the market guide for AI ops platforms. And their definition as is as follows AI ops platforms enhance it operations through greater insights by combining big data machine learning and visualization. And they produce this great diagram that basically talks about all the data coming in, whether it's historical or real time, funneling that through and correlating it and making an applying context to logs metrics wire data and documents. And then a continuous process of observation engagement and acting using big data machine learning produces historical analysis, it improves anomaly detection. It improves performance analysis and it greatly helps with correlation and contextualization. Gardner has thought about this particular category and applied it to network management and has come out with this with a recommendation or at least a forecast that says by 2024 60% of network performance monitoring buyers will require improved AI ops capabilities. And this is up from 30% in 2018. So we can see that this trend of greater complexity, greater embracing and implementation of machine learning is driving a need for AI ops. Now one of the surprises that we got from our survey was when we asked all the network professionals that we talked to about whether they're actively using AI ops today. The majority of them said they're prepared for full network automation and 65% machine learning is important for network management. AI ops adoption is in the early stages. In fact, only about 10% said that they were using AI ops today. So this is something that we found very interesting and we found about it. We tried to figure out you know why if the need is there, why is AI ops adoption kind of relatively low. Well, I really, you know, we think about AI ops. It's really a collection of capabilities inside a platform, you know, and we see that while it's there, the platform availability and the platform adoption is something that we think is going to increase. And we think that our prediction is that more and more network professionals will turn to AI ops platforms to be able to kind of resolve some of the issues that they're having with the increasing network complexity. So quick overview of Kentic. Kentic is AI ops for network professionals. We provide revolutionary analytics to make every network excellent. We have an AI ops platform that takes information from context and labels geolocation threat data routing and business. We incorporate traffic and metrics using net flow and s flow and SNMP VPC flow logs, lots of data for coming from the cloud. And we provide from that queries and insights in an automated way and actions through an integration through lots of different integration with with different vendors and platforms. We're a SaaS based platform. We can function at any scale. We can work with every network and we provide all this information and insights in real time. We provide a unified management platform for diverse infrastructures cloud when SD when traditional data center, all these inputs. We deliver interaction through automation and notifications and allow you the ability to query and investigate your network. So looking back at the survey. Let's go over some of the key findings and some of our recommendations. So first of all, it's a cloud centric world. Again, everybody on this call knows that and we appreciate the CNC F giving us the opportunity today to talk about AI ops automation insights in relation to the cloud. We know that running today's networks is dramatically harder and everybody out there is really having a hard time in many ways trying to keep up and also think do things like prevent outages while also helping the business succeed. We know that adoption of machine learning and automation are both on the rise. And we also know that insights and automation restore manageability. And with that can tick powers up network teams with AI ops and we invite everyone here to download our report. You can get the link will put it in the webinar. It will also be in the presentation that's posted on the CNC F website to take a look at more detail and you can get a lot more interesting insights from the report is including stats around adoption of cloud and multi cloud by industry. So Phil, I'll turn it over to you to see if we have any more questions. Sure. Yeah, thanks Andy. Great presentation. So we've had a few questions kind of interspersed. While Andy presented, we definitely have plenty of time if someone has additional questions so we can, there's nothing open at the, at this very moment. So we can see if anyone dropped something in there. But I'm sure you know if there's other ways people can get in touch with you I know this recording will be online. As well but actually we just had a question is your platform already integrated with past solutions such as open shift. Yeah, I think that's actually a great question. So we talk a lot about orchestration and the importance of integration with orchestration platforms and platform as a service capabilities. Such that you can kind of automate a lot of the insights that we that we export and conversely in just a lot of the context that comes from platform as a service and also the related tools. So for example, information from like labels and tags that come from the cloud infrastructure. We do this today through API. So our platform is fully programmable and you have the ability to program it through API ingest or actually export data out. We've got another question. Do you have your own probes for us to install and an on premise implementation or you or you can work with existing probes. We do have the ability to deploy agents. We do have the ability to deploy our platform. It's available in both a SAS and on premise version. We have the ability to work. There was a question talks about routers. I'm not quite sure of the question there Phil maybe if you want to try. It may be connected. So there's two there. Can we have a demo of your tool. And then it says in routers networks. I don't know if that was meant to be connected. Well, I think the answer is that, yes, you can absolutely have a demo of this tool will capture your information and we'll get we'll reach out to you to set something up. And in our demo we have the ability to incorporate data from your network your own routers and then allow you to interact with the platform and see your data. Hopefully that answers that question. Great. Thanks Andy. Yep. So, yeah, so we've, we've cleared out the Q&A. I think we'll. Yeah, it sounds like Josh. Also, we can collect that name for you as well. Another great in a demo. Yeah. Yeah, great presentation. If anything else pops up in the next 30 seconds will catch it otherwise. I know Taylor and the CNC F will handle get this getting this recording online along with the slides, which as Andy just said has some links there at the end for you to follow up if you want more info. So yeah, thanks so much everyone for joining today's webinar and have a great day. Yeah, thank you everybody.