Hello everyone. I'm Alex. Very honored to be here. once again, I can say, so I am going to talk a little bit about, AI, you know, AI is everywhere. Nowadays you can find it in, I can see in every single aspect of our life. So. as a DevOps engineer. I'm going to talk about a little bit about the impact of artificial intelligence in our daily activities. So this is leveraging AI in DevOps, next generation strategies for enhance and efficiency, efficiency and innovation. So a little bit about, about me. I'm Alejandro Mercado Rice and born in Mexico. I, I do the bobs as I said, technical writer and speaker. Sometimes I speak. So there is, if you want to reach me, there is my, you can see my information right on the slide. So. Talking about some predictions, I don't, I don't like to talk about predictions. I can say, I will rather say perspective because I mean, I don't have a crystal ball, but for sure we are going to see this, this trendings, in this year and the following a couple of years. in the following years. So, so we are going to keep seeing, of course, a lot of artificial intelligence, quantum computing, computing, 5G connections. H computing block, more blockchain, more blockchains, cyber security is a major factor in, in all the industry. The software development industry, augmented reality, sustainable technologies, remote work and collaboration tools, human. Human computer interaction is something that we are evolving at. We, for sure, we are going to see more about that. More experiments, more projects, more technologies. And I can say a lot of these technologies and trending technologies are going to overlap for sure. I mean, you can do human computer interaction based on AI. and I mean, overlapping is going to be pretty common. So Why the bobs and why and where is relevant artificial intelligence that the bobs, practices, we have been doing the bobs a lot of time. You know, you can see this, this, timeline. so right now we can see in the, in the last, Bullet or milestone that DevOps is like a present or regular practice. I can say right now in a lot of companies adopted DevOps because they are seeing the advantages. So what we are doing now is to increase the maturity and the productivity while doing DevOps. So, so, well, this is a, the traditional, I can say, So you can see each one of the software development like cycle phases. So, you know, it's, it's your plan, code, build, test, release, deploy, operate, monitor, and repeat. And this is like an infinite loop. So I bring this to the conversation because in each phase. We can use artificial intelligence. So of course, as a engineer, we are doing more with less. I mean, we can code, we can test, we can plan. We can, and have like this secretary or assistant to help us to, to increase our productivity and be more efficient. Spend the better the time. I mean, you can spend more time in important things. So for sure, this is one of the main advantage. I am not saying that artificial for, for, for, for sure. I'm not saying that artificial intelligence is going to replace humans, but we need this kind of, AI to be more efficient and they are here. So the idea is if you take a look of each one of these phases, there is a tool on the market, open source or proprietary to help us to accomplish these activities. So I just want to, to mention a couple of these, because we have, In, in every single aspect. If, if you consider the, the whole DevOp cycle we have in, in deployment and incident management and, and code testing and quality analysis, and in the monitoring and observability field, we have a lot of tools helping us to improve our, well every aspect. We, we, at them, we are going to have more secure software. box, decrease the, the, the time to repair or even to, to acknowledge the, the Abu on, on production environments. So definitely because systems are getting more and more complex. The landscape are. Cloud, on premise, hybrid, multi cloud, different databases. I mean, there is a lot of considerations, a lot of components, microservices, mono, monolithics, containers, et cetera, et cetera. So there is no. enough human power to attend all of these aspects and to take care about everything. So we need this, this, this tools to, to help us to in our activities. So. One aspect that we are talking, the, the last, it is not new. I, we can say that last couple of years is, is an operation side. You know, having this code, this piece of code on, on production, it means that there is going to be a lot of activity attending users, request, latency. So. We talk now about AI ops. I mean, the operation assisted by, by artificial intelligence, to detect to early detect some problem, even, even, we talk now about observability versus monitoring, meaning that we can be proactive in what is going on with our software. I mean, we can detect if we have a pattern that I mean, is. By correlation, we can know if something is going to fail, it's going to fail because it's taking a lot of resources or latency or it's taking a lot of time. I don't know. It depends on the day. So we can find a root cause, before even it fails, even the, the user, notice. So this is, this is pretty important. Because of course we can, we can save a lot of money. So new frontiers. Yeah, for sure. Well, we are seeing new frontiers in each aspect of the, the, the, the, the, the, the DevOps cycle, like in the project management. we can see a lot of open source or commercial tools. Like I can say, just to name a few, Jira, Asana, Notion, et cetera. They are all based on artificial intelligence, but they are adding some features based on intelligent AI to, to be so, so we can be more productive. This is like an experiment, something that I have been working on. It's, it's, you know, we have these boys skills assistants, in, at home. So, so we are going to see these new frontiers. And one of these frontier is the, the boys assistants. So I started to think, and why, why not? I can handle my daily activities with, with just my voice because I can handle some difference. I don't know, pipelines, clusters, health, et cetera, et cetera. Many related activities to DevOps. Well, I want, I am driving or I don't know, I mean, hands free. So I started this experiment is, is, is at this moment is just running on Alexa. Because I, there's no, no other reason than, than I have an Alexa. so my experiment is about this integration of voice control technology with Kubernetes management. So, so I can same, I can, trigger an Alexa skill just to manage my cluster so I can monitor, I mean, if, if there is an error or the systems goes down, I can notice. with, with an alarm in my Alexa, or I can do some, administrative tasks. So, well, this is a work in progress, but it's, it's something like that. so I can, there is a, an activate voice command, open Kubernetes manager, so I can create a cluster, destroy the cluster to ask for the help. It's some, some, some node is failing. I just want to know about it. So, well, this is something that, if I think about it, I can monitor or I can handle manage every aspect of my daily activities, but of course it's requires a lot of, I mean, instead of using the regular input, that is the keyboard. Well, maybe some tasks can be done through the, to the boys. So, so I think this is. Like the things that I, I want to see that we are going to see in, in a couple of years that we are evolving for sure. So why, because we can have this increases efficiency, we can reduce error in his collaboration, improve accessibility. That is a very important aspect of using voice assistance technologies so we can have streamline workflows and contextual awareness. So yeah, for sure we are going to see this kind of trending, this kind of new technology or some not so new technologies helping us in our daily activities. Project manager, we already talked about a little bit of a project management powered AI tools. in the side of code generation, I mean, when I'm writing code, I can get a lot of help from Any of these ones, choose your favorite one, GitHub, Coursera are pretty famous one. so, so we can be more productive because, well, this is something that we do a lot. I mean, some, some, sometimes we don't know about a method or, or a property. So we come to, to Google it or to, to ask in a stack overflow or other. other sites to get some help. And this is something that we do a lot of. So instead of going to through these portals or forums, we can just write what we need and it's going to help us. I mean, it's not going to replace us. We, we, we have the, the, the wheel, but the steering wheel But definitely it's going to, it's, it's, it's like having a, an assistant, like doing pair programming. So this is in the, in the generative code side, even in the testing, in the testing side, we have a lot of options. So we can conduct any, any type of, or almost any type of, of testing, like continuous, static units, testing, code coverage, mutation testing. Using these tools like JetBrains, Snyk, SonarQube, et cetera, et cetera. So in general terms, we are going to find, we currently find a lot of AA based DevOps tools, like for automated infra provisioning, like, Ansible or in the other side for. For incident response like Splunk, Datadog, ServiceNow, et cetera, et cetera. There is a, a, a, a huge explosion of these kind of tools. So, In the, in the site of infrastructure of ASCOTE, we are seeing right now a lot of traditional Tools like Terraform that they are using now, they are powered by, AI to help us to create a more robust code and to, it can detect if there is not consistency in our, at the moment of provisioning the resources. So, yeah, of course it's, it's in every, in every single aspect of our project. So we're developing like sample. So, so you can take a look of any, any, so if, if you are more on the site reliability engineer side, you have this, service level object at the DNA, so of course you are going to improve the, the service level on your teeth. With this kind of tools. So in summary, server ledged objectives are essential in DevOps cycle, as they provide clear and measureable way to define, monitor, and improve the quality of services, ultimately driving better outcomes for the business and its customers, for sure. So. Well, I, I would like to quote this, AI is not the future, it's the present. So take that in, if you, when I take away, this can be one. a lot of these, tools, are based on logs. You know, logs can say a lot of things and we can find through the logs, root cows. So CloudWatch is one of many, many of the monitoring and observability services that we can find. Well, this is, of course, running in AWS, but any other cloud provider have, have logs. So the importance of having logs is that we can have these AI power tools to get the root cause. So, yeah, that's why I bring it into the conversation. So I drive in the Bob's causes, besides the single Presti practice of AIOps is automated infrastructure provisioning, predictive maintenance, code quality analysis, intelligent incident responses, and automated density. So you can be. Way, way more productive with, with these tools than, I mean, like an attrition now, team that is doing this manual, it's, it's, it's tough to, to measure the impact of, AI in, in DevOps, because, I mean, sometimes we don't even notice that the, the, this kind of tools are using. The, the, these technologies, or we don't have like a metric to say, well, I started to, to use copilot. So I commit, in shorter time. So, well, I mean, it's difficult, but according to some studies, some studies by Garner, we, we, the impact, for instance, in the meantime, to recovery is reduced by 25%. Well, in terms of, you know, this is related to money. So, so we are saving 25%, because the system is, is, is not down. deployment frequency, you know, you are going to have more value because You are streaming the value because you are increasing the deployment frequency by 30%. So, so, well, these are just, to be honest, I don't know how these guys, calculate the, these metrics, but I mean, it's something that we are going to talk more. And it's, it's going to be in our daily dialogues and conversations at the office. So of course there are, challenges, you know, the whole DevOps theory is, is more cultural than practices. It's, it's a long journey and there is a lot of implementation challenges. Yes, I can say. So like, you know, the, the hallucination, sometimes you don't, you don't get the, or the responses or answers that you get are just misleading. It's happened a lot at this point, but that's the idea to train the model to, to get better results. So it's, it's going to, I mean, under the hood is, is a, is a model that we need to train. So, so the data quality is, is, is important. So, so it's, it's, that's where I say that it's a long journey. We are going to train these, these models to get better information, better insights. So yeah, the integration takes, takes time. So you have to, you know, to, to play, to experiment and to spend some time to get this, this advantage. But at the end, and this is for sure, you are going to see the, the, the results of, of these practices based on, or powered by AI, you know, like the increased deploy frequency, reduced mean time to response, customer satisfaction, change failure rate. So, as I said, it's a long journey. The maturity in DevOps, I mean, it's not like the automation is not the purpose. I mean, it's like a consequence of implementing DevOps and DevOps with AI, but achieving DevOps maturity is ongoing. It's an ongoing journey that requires commitment, patience and willingness to adapt to the ever evolving software delivery landscape. So be patient. and that's it. I am going to be around here. If you have any question or you can contact me, there is my LinkedIn information. So yeah. Thank you very much. Bye.