all right let's let's get started now and uh first of all uh best of all for 2025 new year starting we have lots of content for you um so hopefully it be a good uh a good year for all of us uh today I'm really pleased to have uh uh Neil Pearson with us he's going to talk in just five minutes about uh from log file to AI insights 60-year evolution of observability and AI Ops sounds like an interesting program that that's going to be difficult to fit in one hour but let's try um before I end over to Neil I just want to remind you that um this is a munion learn H we call it since we started this program more than three years ago now uh and we have a couple of sessions planned for for the next two months uh we uh we first of all the menion learn are again a monthly Ser that is is is more around thought leadership kind of vendor and product agnostic as much as we can and uh we have topics uh in February which is going to be about uh an interesting use case of fraud detection um in AI done by Jordan Nanos this is going to be February 19th and we already have the links and I know doni in the background from the team is going to put the links in the Q&A so you guys can register uh or keep The Links at least we have a session in March 19th on March 19th about um how we used at HP chat HP um it's a kind of a private uh large language model that we have implemented so it's an interesting use case um I was able to discuss with uh uh the the the speaker of this session at the last discover in in Barcelona and he agrees to to explain how they have implemented then some of the tricks and tips from that implementation so that's for for Munch learn we have two additional programs that we run on a monthly basis as much as we can one is called the meetups so in the meetups as opposed to Mion Lear we try to go a little bit more deeper into a technology an open source technology or a technology uh and we have a couple of sessions already planned for for for that series as well for example at the end of this month on January 29th we have a deep dive on the private Cloud AI the software stack for that um so that's uh one that you might want to join for if you're interested in this solution that we also announced at Discover uh discover in Las Vegas that was uh on February 26 we'll be doing an introduction to HP Green Lake web hooks so this is kind of a way you can subscribe to events from the HP Green Lake cloud and uh and write your own web hook events this will be delivered by uh the lead engineer for for for this feature in HB Green Lake so that should be uh Technical and good enough for all of us so that's February 26 you can already register for that and finally we have something around Deep dive on ebpf and and it's good that Neil is here because he's also the speaker for that one and we don't have a final title yet but um this is an open source technology for doing some sort of sandboxing on Linux carel and uh and and Neil would be talking about that March 26 finally we started another Ser back in August September of 24 uh we called that AI jam series and this is different it's more like a panel discussion on AI uh and we have two sessions coming up one is next week and it's going to be how to build a Nial and thrust worthy AI system and we have a couple of uh panel experts to discuss that uh with orre our uh host for for the session and then on February 12th we'll have a title session entitled AI inferencing at the edge from hear to space which sounds like an interesting session also I don't really have yet the registration for that one because I don't have the abstract and the speakers but I'm working on it so this is what we have for the beginning of 25 I think it's it's pretty uh complete already so feel free to register for those who are available which is basically anything in January and most of the things in February and there are other ways to learn from the hpev community uh we have one program that we call workshops on demand which uh you should be aware of uh this is a a catalog of workshops these workshops are delivered through Jupiter notebooks and this is available over the Internet uh free of charge 24 by7 to anyone um we have more than 30 workshops in the catalog right now from learning a program pring languages language such as python rust we recently introduced a 2011 workshop on python for example uh up to open source projects such as Docker 101 kubernetes 101 so getting started with this technology or even uh HP products of course such as Green Lake API or uh system ability Insight Center so these workshops are available I I I would suggest you give it a try and if you do please provide feed back you know if you like the experience if you if you think there are missing subjects that we should add to the catalog uh just speak up and and tell us through this uh end of session survey finally I'd like to add that this is a community that we are building here and we need your help to amplify and contribute uh so you can invite uh anybody on to these talks they are open to all audiences customer Partners University students friends colleagues so so don't hesitate to invite other people to join our talks we have a newsletter to keep up with what's available each month uh we send that newsletter at the beginning of the month uh so the newsletter for January is out already you can find there all the upcoming events of course but all the blog that we released in the past months and other interesting things that you should be aware um by the way all of these uh newsletter blogs are also published in BHP so if you have with HP and you want to uh share those articles on social Med your own social media uh we have all those articles uh in in the platform so look for HPD for example and you'll find all the Articles from the HP Dev Community which y you can share on LinkedIn or on other social media we have a dedicated slack workspace that is uh dedicated to uh customer and partners and internal folks asking questions about integration about development about um automation um so there are a lot of channels available to ask the question in the right place don't hesitate to sign up you can always help answering questions or you can ask your own questions and finally we have a NX Twitter account that you can follow if you still want to continue to use this platform feel free um if you're anme and you'd like to contribute uh we have a very easy way to write blogs and contribute your own blogs so feel free to uh take a look on this HP developer hp.com contributes we explain how to write a blog your own blog and we provide technical review we provide uh editorial review and I would say within a week you should have your blog up uh on a on the on the website uh available for your customer for your partners or for yourself so if you want to uh write a blog you can reach out uh if you have questions if you want to deliver one of these Meetup uh you reach out to us as well and if you want to work to add a workshop on Dem m in the catalog feel free to also reach out to us we are looking for for helpers here so uh we'll be happy to talk to you and these are all the links uh for reaching out to the dev community so this is the internal the website the internal slack uh the external slack first the internal slack we recently also started the partner uh Slack which replaces the old Yammer uh and we have the monthly newsletter we have an email of course and this is the HP developer X account and this is direct link to our workshop on the M you can you can scan that QR code to get all the links in one web page and with that I would like to hand it over to Neil now the whole audience has arrived so it's all yours thanks okay thank you so much D for that amazing introduction um it's truly uh an honor and privilege to be able to speak to you all today um for those of you that don't know me um my name is Neil Pearson I've been with uh the opsramp uh company now for nearly five years um but I've worked in the what we now call observability AA Ops space for the last 35 years um all my working career actually and it still fascinates me um this subject um but it goes back way further than that you know this is why when I research this we're going back nearly 60 years um when the term observability first was you know came out so what I'd like to do is whether you're a a newbie a rookie in this area or you knowme uh I'm going to explain to you where we are now um I will cover opsramp of course because that's where I'm focused but obviously there a much broader um set of solutions out there in the market I'll explain where we are today with observability area Ops but how did we get to where we are today what have been all the Innovations and the the developments over the years um so I've done some research into this um so I'll go back to the 1960s all the way to the present day give you a glimpse of what we think the future holds now DDA mentioned at the beginning we will be doing a talk on a tech technology called ebpf which we believe is going to be incredibly important going forward incredibly important technology in the observability space so more on that um in March so without further Ado let me just come on to the slide so I'm actually not using PowerPoint today I've decided to do the the presentation within an observability platform itself uh and that is opsramp okay so here we are so I want to just talk about um as I was putting this together recently way back in 1993 um it's when I really started to get interested in automation so obviously that's you know three decades ago uh but everything that I've done up from then until now has has been consistent as everything you know all of the the solutions and the the companies that we speak to um in our day-to-day jobs it all tends to focus on automation um I don't know about you but you know I follow a lot on social media and YouTube Every everywhere I go now they're talking about AI agents okay so any task that needs to be performed there's there's a there's a chat gbt or Claude or all these type of you know new Solutions that's going to enable you to get smarter you know do carry out tasks quicker than ever before and this is only going to be you know continuing but I first started to think about this back in 1993 I tell you a little story very briefly so I joined a private Hospital in London um I was a junior uh you know computer operator and I went for the interview and I thought it went really well um but it I didn't get the job and I asked the hiring manager hey you know I thought things went really well um you know could you give me any feedback and he told me that he didn't like my shoes this is a true story I didn't get this job because uh the H manager didn't like my shoes however the guy who did get the job never showed up for work um and then I was you know there ready you know to take that role and so I joined and it really was the springboard for what I've become and what I do today um so what I did was um I was working on a platform called deckx using the VMS operating system for those of you that are millennials you probably won't really know what that is but um this is one of the distributed systems that came out in the 1980s uh but it was still it is still used today uh in fact uh in banking industry uh to a small degree age but what it allowed me to do was to think okay uh I'm running operations or trying to you know do batch processing and all these things but what I figured out was that every single task that I was asked to perform could be automated using a scripting language so that's what I did I built an automated operation system to effectively I just clicked a button and it carried out all my duties for the day um and after a year um I effectively made myself redundant so I moved on to a new role where I did exactly the same thing um so the dream has always been around automating operations we call that today in opsramp autonomous it operations so it's not a New Concept obviously the technology and you know what we do these days is very different than 30 years ago um but the fundamentals are the same we now got it's all about AI agents you know doing doing everything for us so this is really where it gets exciting within operations is how can we you know um evolve to allow you know Ai and machine learning to make recommendations on what actions to take what I want to see is it's a bit it's not as farfetched as it sounds is I want to be notified I want the the computer the system to talk talk to me so today we have like chat gbt and all these type of solutions you have to ask a question it there goes fetches information and returns you know the results we're going to get into a stage bit like in Star Trek very science fiction the the system will talk to me when there's this issue and recommend what actions to take and then I'll be able to respond uh and so on that's the dream and we're almost the there I kid you not so just to give you a sense of you know we believe what I'll come to the Future right where the trends are you know as I further further in the presentation but what is currently defined as the future of observability in aops it's already here with a solution like opsramp the first one is around unified observability otherwise known as the single pain of glass so op if you're not familiar with us we've been in business now for 2014 obviously we were acquired by HP two years ago or nearly two years ago and what what you're seeing on the screen here is a single view into your hybrid infrastructure so everything from end user experience using what we call synthetic monitoring all the way down to bare metal hardware monitoring and everything in between this is what um we are delivering today that is Unified observability now secondly some of the advances is being able to visualize an IT system okay so one of the techniques that's been around a long time is ability to do Discovery and dependency mapping this is that so this is an entire vmw infrastructure that is automatically discovered and this is used for several purposes first one being to do better change management so being able to says okay I'm going to update you know uh some some storage what would be impacted by that change and then you can have better you know uh roll back planning and the Second Use case is around machine learning to do alert correlation and I'll talk about it about that a little bit more as we go along so the ability to do you know Business Service mapping again this is something that is so important you know being able to offer customers the ability to see exactly where in you have a business service like you know software as a service or you if it's on premise this is an Erp system great sap for example and then what are all the underlying IT services that make up that system and so you can see exactly where the issues are this is called Business Service mapping and it's very much related to topology now ai Ops we tend to focus on you know what we call event and inent management but that's not just what it's for it can be you know AI Ops is about applying AI machine learning to any type of operation okay that customers perform so but we tend to see the best results in the event and interet management area so to give you an example of what is you know perceived to be you know something that is difficult to obtain but we've had this solution now for several years so being able to reduce noise in the environment okay by up to 99.97% as you can see here this is what we're talking about to become you know much more efficient you know doing more with less reducing the noise and frictioning the environment reducing falce positives machine learning allows you to do that and automation being able to automate any type of activity or task this is what we're talking about okay this is what the market is looking for opsramp and you know there are of course there are other Solutions the market but we HP but the final thing is um again this is where the future Direction and the dream starts to become a reality we're already on this path so using AI to ask questions and then get the results so this is built into the platforms um but again it relies and obviously the system having all of the data the Telemetry data to work with alerts metrics logs uh traces Network flows and then then being have the ability to ask questions about that data that's where we currently are uh but the ultimately we want the machines to be able to speak to us that's my dream whether or not we'll get there I don't know but I hope we do because for those of you that are working with customers or you are a customer there's just too much complexity going on there have too many activities that need to be done it's very very difficult um to manage it operations these they given the complexity let's use these platforms effectively because this is not going away as I said I'm we're bombarded now with AI agents to do everything browser automation um you've probably got experience of this yourself this is the future okay and observability and OB is no different but so this is where we are now okay we have single platform for single observability and a platform and automation but how did we get here so you have to go back to the 1960s um to to look at this you know where did this all come from it was actually introduced in 1959 okay not 1960s so the if you if you're familiar with the definition of observability it's a it's about trying to look at the inter trying to understand the internal state of an IT system by the outputs that it produces and this concept you know was introduced way back in 1959 so this is not a new term obviously you know the practicalities of it today are very different but the concept Remains the Same we want to understand what's going on in our systems but the only way to do that effectively is to analyze the output we like to look think of this as a there's a there's an acronym for this called melt so metrics logs traces that's the modern day but back then very very different obviously technology very very rudimentary and fundamental um so what was actually going on at the time it was really Engineers trying to look at you know me you know mechanical systems to understand the the internals it was really the uh it was you know really focusing on Aerospace and Robotics and but what they did back then if you're familiar with the history of this is fundamental to what we know today and without this we probably wouldn't be having this call and this meeting so what were some of the key advances um as I've been doing the research here that really enable us to do what we are doing today so it was dominated by mainframes okay particularly the IBM system 360 just the cost of things back then was just astronomical so memory was like for8 kilobytes of memory was $133,000 I can't even think what that is in today's money it was the first time in 69 that you know um two uh computers or nodes effectively could you know communicate with each other and that was called aranet between UCLA and Stanford universities apparently the first message I didn't know this they they typed in a message called login but the system crashed after it got to L which I thought was quite interesting so that effectively was the birth of the internet as we know it today and that was way back in ' 69 it was also a time when you know time sharing uh evolved where a computer allowed multiple users to access the system simultaneously and it was the first time we started to see monitoring tools okay was way back then something called a tool called IBM monitor 2 and it was a very very early monitoring tool for mainframes there was obviously a lot going on at the time there was a boom in the computer industry you know technology was advancing rapidly you know new program languages were coming out uh Cobalt being one of them um I I think if there are any cobal programmers still out there they're probably multi-millionaires by now because there's just no one is really learning this language anymore but many it system still r on it and you know again there was um apparently the mouse was I invented in 196 1968 it just seemed such a long time ago but without all of these you know advances we wouldn't be where we are today things started to evolve not not very much in the 1970s uh when I was researching this again very very rudimentary and early development monitoring tools mostly for mainframes because of they were dominating the market at the time and it was used to track basic system performance and resource usage again these type of computers were largely used by Enterprises and obviously you know uh government agencies in in the uh industries that you see on the left here and if you were a system administrator back then um you obviously very very rudimentary tools that you had available to you log files were you know available but again what I found in my experience is you know log files like that it comes down to the developer you know and the engineers who built those systems to you know expose that information obviously you know we take it for granted today um but so back in the 1970s um this say I was not working then of course but um some of the key advances back then that really helped us today Unix was born in 1971 um I forgot I thought it was much later than that but no it wasn't um the birth of the first personal computer came out um back in 75 called alter 8 8800 ethernet was born in 1973 again I thought that was later um little did I know so lots of advantages L lots of advantages sorry so new new program languages came out um C language again lot of still the applications today are running these languages big growth in the computer industry um email came out in 1971 again all the things that we take for granted but these were the without these we would not be in a position where we are today and they were the the humble beginnings of what we call observability today so that's just the 1960s 1970s so sorry it's timed out we are live apologies for that so moving on to the 1980s and 1990s so this is really where you know kind of I I kind of started out my career so 1980s come along um introduced the adoption of the SNMP protocol again we're still using that today um it's up to version three now so a lot of what we do J opsramp and network monitoring is still based on SNP that's not going away and but that's been around you know since for nearly over 40 years new monitoring tools started to be introduced tools like big brother I do remember that um from from early days enmon were being used to monitor performance so starting to see a change um in what was going on and particularly with the systems that were being developed there was the rise in what we call distributed systems so this is what I'm I'm more familiar with so digital deck VX is an example of a distributed system um we started to see because Linux uh Unix was introduced in the previous decade we started to see the usage of scripting languages um cron as a way to automate jobs again I you know as I was researching I learned some new things I thought KRON was was later than this but no it's been around since the 1980s and we still use this today to automate repetitive jobs so some of the key advant advances and Innovations in in this period that are relevant to us again you know DNS was introduced the main no system in 83 the internet as we knew it was really formed in 1983 with the introduction of tcpip If you've ever watched Mission Impossible um you'll see Tom Cruz using uset and bulletin boards but again this was in the previous decade so that film came out in 96 this is in the 1980s this has been around so again without any of this you know what we're doing today in in in in this world observability nothing of that would be possible clearly some some big advantages advances in you know Computing you know cray 2 which is obviously we're we're familiar with here at HP some new programming languages that again I am very familiar with in a pearl and if any of you have used that before not so common these days or popular um it's more python these days is is very common that we used but I spent a large chunk of my career you know programming in po um again uh I started using that in 97 so but it' been around already 10 years again all of these things are used to help automate you know repetitive tasks um and these are just fundamental uh as we will as we will see so the 1990s really um maybe many of you started your career in the 90s uh where we started to see tools that are still used today uh with many companies like nagios IBM tiv you know BMC Patrol um and obviously the HP open view which is obviously no longer which is still used today okay but again a lot of these we I don't like the term they call them Legacy monitoring tools but they are still widely used today um but clearly a lot of customers and projects I work on they're slowly starting to evolve away from those types of solutions to more modern equivalent um because the the market has shifted significantly complexity started to rise uh with uh distributed systems so it was more about focusing on reliability and performance of the the systems that were interconnected with each other we started to see the introduction of you know logging solutions to agregate logs uh centralized CIS log um and tools like that started to be widely used because you know logs are really the gold mine in in in our world this is it's probably the most useful information when troubleshooting issues and that's really you know a big you know requirement these days uh but it always has been is to identify the root causes of issues I have my own opinion on this I think a large number of of outages and downtime are caused by um change management Okay so so you know if you ever see the news and there's been an outage typically the root cause is a a a configuration change or some kind which is usually the root cause but it's not always but you know we're starting to see progress by using Telemetry data such as logs and metrics to help identify root causes of issues and clearly there was a massive um expansion in the internet so you know from the early 90s when I first got exposed to this um started to see you know obviously huge advances so um what was going on at the time relevant to you know um the topic today obviously the web was born in 91 okay we started to see you know the emergence of web browsers Netscape Navigator do you remember that um clearly massive boom in in the internet in terms of the users um PCS um Windows 95 started to emerge obviously we had Windows NT as well um at the same time we're starting to see e-commerce okay you know Advance with the likes of Amazon and and eBay start to see Advanced networking so the Wi-Fi standards started to be introduced uh again new program L is coming out Java 95 HTML JavaScript uh in the middle of the decade so again what you're seeing is from the early days there's just been incremental advances in the way that we manage it systems given the advances in in the technology so moving on to modern day observability so starting with the 2000s and up until the end of the the last um decade so around about the 20012 2002 we started to see the emergence of a technology called APM or application performance monitoring so some of the Pioneers in that space New Relic Dino Trace um really started to revolutionize How We Do application um monitoring and observability and obviously there there're still obviously key platforms today um there's been a a psychological shift from from what we call reactive to proactive monitoring so I like to think of this as the brake fix model is you know we we're monitoring our systems unfortunately something breaks and we fix it okay that's a break fix model so they're starting to see okay um are there better ways to to do this to get in front of issues and we like to think of that as proactive monitoring okay so there were um vendors out there uh one of them that I remember is called maturative that they were really Pioneers in Predictive Analytics so being able to you know have a data Lake and being able to analyze a lot of data and being able to determine you know um unknown unknown issues and you know I'm I seeing you know trends that okay are they seasonal and then can I take any type of preventive action so you're starting to see the emergence of these type of solutions given the you know the complexities that exist we're starting to see you know big Traction in the devops you know uh movement so rather than it operations being a silo and the development team being a silo we wanted to try and break down these silos um and that is still a challenge today um although the adoption of cloud native uh and site reliability engineering as trying to address some of these issues but it's about you know um those two groups you know joining together it may even be the same type of it may be the same person performing a lot of those duties but it's combining you know the the best practices around you know development operations and it made a lot of sense we're starting to see more automation um happening in the in in the industry um more and more tools are being introduced to the market um and of course uh virtual virtualization uh was introduced so again some of the key advance advances and innovations that were going on at the time broadband and know really became mainstream um big rise in social media platforms um I actually thought LinkedIn um came out in 2007 but no I got it wrong it's 2003 so I was four years behind the curve um on LinkedIn um some older Technologies Myspace Google and and so on really started to you know dominate you know the social media environment do com bule uh com bubble happened uh between 20002 2002 obviously lot of lot of issues there that's not really relevant to observability but what is was the growing adoption of open- source um Technologies particularly around Linux again that's really the mainstream today um I don't really obviously still see Windows um with customers but majority of workloads now are are going to be running on on Linux platforms so some of the um advances in in program languages which are relevant today particularly around JavaScript Web Web 2.0 um a lot of the automations uh that
we use today are using using JavaScript and those type of prag languages but again you know this is nearly 25 years ago um so this you know this has been happening a lot so moving on to um the 2010 period so we're starting to see the emergence of observability as a distinct discipline uh within you know uh an IT organization so the idea is that is based around three pillars you have uh metrics logs and transaction traces so that's the Melt term okay so that's really what we are basing everything on today and the idea is again going back to the 1960s it's about trying to you know understand what is going on in our systems the internals by the output that it produces in the form of metrics logs and traces that is observability the natural but in the middle of the 2010s around 2016 Gartner introduced the term AI Ops or AI for it operations again that is still very relevant today uh it opsramp here we refer to this as autonomous it operations but again it's really you know the what we've been doing within the Ops platform is really around aiops okay but what we're doing is we're combining observability aops and automation into a single platform to give customers you know a single solution uh for all their needs so we're starting to see you know a big increase in the adoption um of aops around about until 2020 okay again that kind of coincides with the the evolution or emergence of observability as a mainstream practice um so again just very briefly on some of the key advances I won't go through all of these but we really started to see the emergence of devops um and what was called cicd pipelines so again if you've been involved in the development Community you know this very well I think this is really key to observability um because if we're able to surface you know changes because there there's just so many changes happening now given the uh the complexity of current you know it systems we've got to be able to understand changes and being able to link that to any Downstream performance and availability and reliability issues so we can identify the root causes um immediately and so that's where you know whenever you're going to hear me talk and if you see me demonstrate you're going to see me start talk about cicd integration as I think that's really key so moving on briefly to where where are we today in the last few years so we're starting to see a massive surge in the the observability in aops markets just to give you some figures here um from up until you know 2022 the market was worth 2.9 billion and by 2028 it's expected to reach you know $9 billion um so there's a huge interest still um in this type of solution given the benefits that it offers you know uh customers and you know Enterprises today again a aiops is seen as like a separate uh Market but I think that you know we this is really converging so rather than having lots of different tools you know a lot of the you know our value proposition opamp is you know why do you need so many different tools you know you have a best to breed solution that combines observability aops and automation but there is a separate market for that so um from the research you know 70% of organizations um have already implemented or this or have a plan to implement observability um with 92% you know prioritizing as you know a key initiative so this is over the last five years um of course if you any any organization that you talk to or you work with you know just have a look at the Investments that you've been making over the last 20 years I'd be very surprised if you don't have some kind of observability and a Ops solution already and other monitoring tools but you know what's your strategy going forward given some of the trends which I'll talk about in a second obviously one of the challenges with observability is just the amount of data um so if you have you know an application that's generating you know um terabytes of transaction data daily do you really need to be analyzing all of that um to really understand you know what's going on inside your system maybe not so there are some challenges around the data and how to manage that data uh and there's still challenges around silos so this is normally uh in my experience related to investing in different tooling so for metrics there's lots of different tools for that you know compute you know storage you know different solutions 10 different logging tools you know you know tracing tools that's really one of the challenges that we still see um so that is probably part of the strategy as okay how we're going to tackle that again lots of advances and innovations that are relevant um to where we are now it's going to be interesting to see what's going on with with Quantum Computing and you know large language models how we can apply you know observability in aops techniques for these new technologies um that's what makes it really interesting obviously huge boom in AI again we're just bombarded with AI okay um but how do how are you going to apply that you know to managing it operations or complex infrastructure or complex application environments or if you're a service provider how are you going to you apply that technology to managing thousands of customers um and that's really you know the direction the market trend is to address some of these issues so it's a huge huge you know potential um in the market now this is really gets interesting so in terms of the trends um this is you know you know done by independent research these are not my words uh just the research that I've done uh this is not opsramp talking here this is what the market sees as the future Trends but if you read it AI driven observability we want to use AI machine learning to analyze vast amounts of telemetry data in real time identifying patterns anomalities and root causes that is aail able today okay we're able to offer you that today Predictive Analytics you know using AI to predict failures we are able to offer that today okay self-healing systems you know integrating with automation Frameworks again this is available to you today so a lot of the tools that um I'm personally familiar with that customers use are tools like anible terraform um and other you know infrastructures code tools they are can be used effectively um to handle automations with endpoints because you already have them we want to reuse those Investments that you've made you why do you need a point you know automation tool to handle that when you've already got them and the idea here is we just want to integrate with them but the self-healing part again this is not a new idea we've been wanting to do this for decades um it used to be the term most called autonomic Computing back in the day so thinking machines so this is where I'll come back to the dream is okay we're going to get to a point where we'll have a single platform we're ingesting all of the required data um for observability metrics logs traces Network flows and so on we're we're going to get to a stage where we're going to be able to make decisions and rather than me worrying about you know how my system is performing it will be able to tell me physically speak to me that there's an issue and it will come up with obviously the recommendations and I can speak back and says you know that will be you know part of the self feeling you just allow it to make these decisions or you have the controls in place so we have that today with the opsramp the ability to have you know um some controls in place where to say okay before an action is taken you need to get approval um so we have the fundamentals of all of this today unified observability platforms again this is an aspiration having the single pane of glass it's been you know a consideration for many many years is this even achievable of course yes it is um you know opsramp is a un unified observability platform so you have all of this today you don't need 10 different tools to achieve this cross environment monitoring you know having a single platform that that manages you know hybrid cloud multic cloud on premise it says the it says the future trend is it will become standard but that again is available to you today right so increasing focus on developer observability so the term shift left has been used uh for several years now but again it's about you know trying to get in front of issues as early on in the development cycle as possible so let's not wait for you know production issues to surface let's try and get them in pre-production environments okay um and that's really obviously you can do that today okay but you know it's using tools like or Frameworks like open Telemetry so this is a cloud native project that op and other vendors are fully fully on board with um and if you ever seen me demonstrate opamp solution you'll see me talk about you know this this uh project here so again the the modern day you know the the emerging solution is EF so if you haven't researched it you know go look it up um it's going to be you know one the next talk I do on here but again that's going to be really critical going forward um to integrate that data into you know to give us in even more insights into how our systems are performing so data volumes are going to just you know um get even more more uh massive okay so today you know the Last 5 Years you know the the data tells me there's 2.5 quintilian B of data daily that's even just going to be minuscule in comparison to what the future lies now the last one um I'll just talk about here observability for business metrics so this used to be known as business service management BSM uh for those of you that um have been around a long time but for those you not with this has been an aspiration for for a long long time being able to align the business with it okay but in order to do that we need to be um understanding business Data Business metrics business kpis you know customer satisfaction you know Revenue non-it you know kpis but combining that with what's going on with their it systems now observability has all that Insight okay we we've understood the internal workings of the system by the output we can now tie that in with you know business metrics again it's um available today but um maybe just customers are just not aware of this or it's not you know on their road maps yet but again these are all you know um aspirational you know projects uh to focus on but you know be rest assured that you know is a solution available today so I'll um just in a few minutes I'm not going to spend too much time obviously you know going through the opshan platform but just to give you an idea of you know the solutions today so UniFi observability yes you know we have all of the data right the metrics logs traces Network flows we have all of the Integrations into all of the Y premise you know hybrid cloud and Cloud native environments thousands of Integrations we give you the ability to understand what is going on in your environments so to give you an example of that here's a VMware um environment okay I'll just zoom in and show you um this view here you can see the inner workings of that entire system okay this is just discovered automatically okay this is what I'm talking about so this allows you to you know understand okay the impact of changes you know where issues are but machine learning uses this to identify um the relation the relationships between configuration items again very very critical um to to you know achieving the outcomes that customer looking for so I'm not going to go through all the capabilities but one one thing again that we are offering is the ability to use geni um to ask questions about the system so this is really the future uh Direction so the dream is going to become a reality um so next part we offer is that AI Ops op we call it AI powered analytics but it's really where you're applying AI machine learning um to various operational processes again where we've really focused on initially is on the event management The Incident Management process giving you the ability to predict um when issues are going to happen based install it seasonality patterns streaming out alerts to third party systems so this allows you to reduce the noise in the environment so to give you an example U live so this is what I was showing earlier on um so here you can see you know over the last 30 days there's been over 24,000 alerts we've been able to reduce that down by 99% to give a small set of actionable alerts that then if we can apply automation um we this is what we refer to as autonomous RT operations okay so we're just letting the system run by itself okay so that's when we apply intelligent automation so we provide the the framework to develop workflows okay and we can embed all the scripts integrate with infrastructures code tools do all your network configurations backups embedding a knowledge base providing patch management all the type of automation that you need but the the the value here in these type of solutions is we combining it all into a single platform so you no longer need 10 different tools to do this okay um that's all I really had today um I just wanted to thank you for giving me the opportunity to give you a sense of you know how we've evolved you know from initial days of Main frames back in the 60s where you know log files was really the only thing available or very very you know rudimentary log files um all the way through to AI insights um and the future direction of this of this really really interesting space um I just want to thank you again and uh I'll just pause there and um we hoping if there's any questions I'll be happy to thank you thank you Neil there's a number of discussions in the chat about uh interesting books uh about history of computers so that's that was interesting but somebody is asking uh Ken is asking about uh what llm is upstrom using for chat and alert automation so um I would I need to I believe we're using uh Lambda lamba um but I I will check if there's a way that we can get back to this forum uh with there need answers to questions um I will find out but what I showed you in that screenshot is not yet released it was announced at Discover uh last year it's called the co-pilot again um happy to go you know get some technical details around what we're using but again it's still in development um and there's lots more that's coming out from us around Genai we're really on that Journey um towards being able to you know ask questions so this is that AI agent um that's going to be but it's based on the the data that's in the platform but I will find out um the exact um llm we're using and okay and there's there's another question in the Q&A and here we have the traces uh yeah uh loo is asking how complex is it to deploy Ops Ram so I will provide you with the the email of Leno so you can provide the answer there yeah absolutely yeah Ken is trying to find out if it's meta Lama 3 is that what you looking for yeah okay yeah I I believe it is but I will I will confirm that okay uh and I think that's that's about it we uh we provided the the weren in oh let's see there's another one oh that's a complex one I don't know if you're seeing the Q&A from the question from Justin about autonomous cars ah yeah that's a tricky tricky one wow okay you thinking about that I I brought up the end of session poll everyone so if you can just spend like two seconds answering those three easy questions so we get feedback from you about the the session thank you very much yeah so the last one I saw you using demo is it HP internal yes it is it's on the HP demo portal so if you log into that and then just search for opamp uh you will see um the the demo solution that um I'm using right here yeah i' have to uh uh think about the the self-driving car uh yeah mat in the chat is reminding us that we have open a new channel in the slack workspace the HP Dev slack workspace for it's called HP opsramp so you can ask question there or answer a question there um feel free and the link is in the in the chat so people can maybe continue the conversation there that would be good yeah well thank you very much for the questions uh we will get back to you uh with sensible answers um the one about the self-driving car that's that's interesting analogy um and but thanks Justin for for that one anyway thanks for for everyone for joining I think that was a very interesting session thank you Neil for uh spending the time with us uh we eager to get you back uh in March about this ebpf session me too and um again thank you everyone for joining we'll see you in other sessions throughout the month or next month for the next MCH and learn thank you everyone and have a great rest of your day thank you everyone bye appreciate it
2025-01-31 04:54