Generative DevOps Hybrid Skills in the Age of AI | DevOps Experience 2023

Hello everyone. Welcome to DevOps experience. I'm hoping that you're enjoying the session today, uh, because the content and the speaker has been amazing so far.

I'm really delighted to be here, uh, being a speaker and sharing insights on the topic of generative DevOps hybrid skills in the age of ai since generative AI has been trending throughout. So I'm sure as part of the DevOps community, many would like to know what's trending in these skills. So that's my intent, uh, to provide, uh, more insights and more experiences from my side as such.

So without further ado, let's, uh, deep dive, uh, further as part of the, uh, session. So, uh, a brief intro about myself. So my name is RIS Neal, and I'm currently the head of corporate development at Practical DevSecOps, which is the world's most loved, uh, practical hands-on, uh, uh, online library and access and subscription, uh, towards the Dev, DevSecOps and product security related courses.

So if you have not seen it, please feel free to, uh, visit the website. Along with that, I'm also the DevOps Institute ambassador and C B F Ambassador, and have been a frequent, uh, presenter, organizer, and speaker at various local and international events. But more importantly, I'm a continuous learner and at DevOps, enthusiast by heart, just like many of you.

So, let's, uh, set the agenda as part of the overall this session, which we have. So we have quite an extensive session as part of providing, uh, different dimensions about gen generative DevOps and how the role of AI and DevOps is emerging and has been transforming, uh, overall DevOps, uh, transformation within the organization as well as, uh, the individuals. And then how our DevOps engineer role has evolved with different key skills with the advent of, uh, generative ai.

And then we will also shed some light on some of the success, which, uh, many organization has so far with the generated DevOps teams and hybrid skills, while also we discussing some of the channel challenges, which, uh, the organization and professional has. And then, uh, eventually we'll provide, uh, some of the insights about how you can gain DevOps hybrid skills, why it is so important to future proof your career. So hoping, uh, that you get the overall, uh, overview about the whole session.

And, uh, uh, like I've shared many times in my prior, uh, session as well, that initially I would like to share a lot of facts so that you get the, uh, criticality of why it is so important to gain DevOps hybrid skills in the age of ai. Because ultimately you have to decide the choice has to be yours. And, uh, uh, my, uh, overall intent is to provide all that guidance, uh, along with the facts to you so that you can make an informed choice as part of your advancement.

So how, let's begin the overall, uh, deep dive into this session. So before we move forward, uh, let's look at the, uh, different waves of innovation that has, uh, embedded into the technology. So if you look at right in the 1780s, the first wave was about, uh, water power, textile iron.

And then the second wave, right after almost 50 to 60 years was around steam rail and steel. That was the primary innovation. And then another 50 years, uh, it took to come to the waves around electricity, uh, chemicals, uh, internal combustion engines.

And then another 50, or almost 50 and 40 years it took for next wave, which was around petrochemicals, electronics, aviations. And then we saw a reduction in the overall, uh, timeline based on the new wave. So when we came into the digital network software, new media, and the current wave around digitization like ai, internet of things, uh, uh, also the meta words, robotics, uh, clean tech, uh, and deep tech and green tech.

So it took, uh, almost half the amount of time where it, it, it used to take initially. And that's how, based on, uh, what has been happening in the innovation cycle, uh, many of the organizations C X O, uh, thought that the future of ai, just like the future of cloud, it took a, a, a long period of time once it, uh, hit the, uh, growth and maturity stage, thought that the future of AI is, uh, really, uh, uh, farther down the line in terms of the timeline, uh, also aligning with the, uh, period of innovation. So then came generative ai, as we all know, with all the buzz and all the criticality and the benefits and the, uh, challenges as well along with it.

And, uh, if we talk about, uh, the overall transformation which we are witnessing in the industry, it's not just go the software that is going to eat the world because it's being said that every company is a software company, but I believe now every company will be an AI company based on the overall transformation, the advancement, which we are seeing in the field of AI and generative AI and transformative AI as part of it. So what I did was kind of encapsulated some of the finding from big four. So Boston and Consulting Group, uh, McKinsey, Forrester, Gartner, kind of, uh, the effort is to provide you a overview about why it is so much of importance as of now, why we are at the cusp of something revolutionary, uh, which the technology, the overall, uh, industry has witnessed in its entire lifetime.

So if you see here, uh, what are different set of technology and domains which are likely to be disrupted, uh, is so you see, uh, the creative workflow, uh, DevOps, ml ops, cybersecurity. So that's where practical DevSecOps, like I was mentioning earlier, come into picture to providing their essential skills along with developer productivity developer experience. So you can see that it is, uh, impacting not just the infrastructure or data management, but also operational, uh, tools, applications in the wider, as well as the, uh, depth of, uh, technology domains.

And that is how if we, uh, look at various tools and the potential, uh, geni use cases in software development within the next three to five years, it is going to be really exponential, right from the, uh, planning stage to the, uh, analysis design, implementation testing and development and, uh, production stage. So for in each stages and in each cycle, it would be, uh, not just, uh, minor transformation, but a major transformation as part of the entire life cycle. And that is how Gartner has predicted that 50% of the software engineering, uh, leader role will exploit, certainly require oversight of generative AI by 2025.

So that's where the hybrid skills approach that dynamic skills approach will become really critical. And that's why my intent and the topic of the, uh, day is to provide that kind of guidance to the wider community so that they can be better prepared and future proof, uh, their career and roles as part of different domains. So when we talk about the Gartner hype Cycle for emerging technology, which was just recently released a couple of months back, and I'm sure many of you have, uh, seen it already as well, but would like to again, highlight the four domains which the Go Gartner hype cycle has highlighted, uh, based on the emergent ai, uh, the developer experience for pervasive cloud and human-centric security and privacy.

So if you look at the overall hype cycle, we will see majority of the weight is occupied by ai. And that's how we are, uh, witnessing something really revolutionary, uh, when we are talking about the time in the entire innovation cycle as well. And if we talk about, uh, developers as such, so the developers productivity with generality AI will increase at such a rapid pace, which it has not seen so far, uh, as part of the overall transformation as part of the, uh, DevOps evolution as part of the security DevSecOps evolution.

So it's definitely going to be very interesting when we talk about developer experience as well. And then talking about, uh, Turing bots. So not sure if many of you have come across the term Turing bots, but it is actually the term which was coined by Forrester, in which they, uh, included the AI and generative AI for software development as touring book bots.

And what they have witnessed is now that the maturity of touring bots has definitely increased to five to 10 years with the evolution of generative ai and the transformation and adoption, which we are seeing through generative ai. So that's how developer experience will make, uh, the overall, uh, uh, uh, uh, evolution with generative ai. Really interesting because the impact it'll have in the entire SS D L C is something which never been witnessed in the entire lifetime of S G L C as well.

So with now all the big four, sharing all the interesting insights on the, uh, uh, generative ai, uh, AI with, uh, DevOps. So it's a lot of excitement, which we are witnessing in lot of, uh, anticipation in terms of what we will witness in coming years as well. But let's also look at the introduction around generative DevOps before diving into generative DevOps hybrid skills.

So, uh, it's an amalgamation of AI with machine learning so that it, uh, everything becomes self-improving, self-optimizing self-healing, and the entire, uh, methodology and the entire objective behind is to make the overall system more efficient, agile, and resilient, while at the same time making, uh, security and, uh, ethical and G R C being one of the critical components of the same as well. So when we talk about the role of AI and DevOps, which we touched upon in the prior slides as well, that it impacts a lot on the automation side, uh, predictive analytics side, uh, continuous monitoring, uh, side, uh, and there are, uh, additional domains as well. And I try to make the slide as informative as possible because, uh, during the virtual session, the injectivity is always limited.

So my intent is to provide you a lot of guidance, uh, through text as well, so that you can reference, uh, what are the key aspects while we are talking about into different segments as part of it. So right from the automation, and we have been talking about a lot about zero touch automation, which is like a nirvana stage for, uh, self-healing systems as well. And using the, uh, data analytics, predictive analytics to predict about a different set of, uh, development and deployment pipelines and enabling the problem resolution as such.

And then also talking about the optimization quality as, uh, assurance, self failing, data-driven decision making. So like we were, uh, discussing earlier that it is going to impact the entire SS D L C or the entire value chain system when we talk about different domains, uh, different stages within the, uh, SS D L C, from plan, code, build, test, monitor, release, deploy, operate and production. So, uh, the overall role and impact is really wide, and that is how the key skills that are required to become a generative DevOps engineer is not just limited to technical skills.

You also need to have a lot of soft skills. And what I try to do in this slide was kind of encapsulate majority of the technical skills or soft skills, which are not just limited to the list, which I have shared here. It's not exhaustive in nature.

It's definitely dynamic in nature based on how generative AI and generative DevOps will progress. But definitely technical skills around coding and scripting, uh, cloud computing, uh, with all the, uh, major cloud vendors, containerization and orchestration, including the Kubernetes automation tools, which include Jenkins, uh, CircleCI, rev ci, uh, monitoring and logging experience with, uh, lots of monitoring tools with the Prometheus, Grafana and log management tools, a version control with GitHubs and, um, which, so, and have the capability to track code changes collaboratively. And then when we talk about soft skills, so just look at all these soft skills, which is mentioned here while I take a quick seconds water, uh, sip break here.

So you can see that the soft skills are also very, uh, V-shaped in nature. So when we talk about the depth in our skills, we talk about T-shaped, V-shaped or calm shaped, in which the top of the tee is the broad general knowledge why the, uh, bottom of the tee is the depth of the knowledge you need. So collaboration, communication, problem solving, continuous learning, adaptability, teamwork, time management, uh, leadership, customer centric approach, ethical consideration, and just not limited to this list, but there are also additional lists, but I've also tried to align it with what, uh, the World Economic Forum has shared in their, uh, future of jobs report as well, which is also very comprehensive report, if you would like to refer that as well.

So why it has been essential. And when we talk about some of the use cases from different companies. So, uh, everyone, uh, know about the success which the Netflix had based on how they mastered, uh, dev was practices, their use of chaos monkey, uh, chaos Monkey, chaos Engineering, and, uh, so that the system is much more res resilient and having that proactive approach to system stability.

So they also use a lot of predictive analytics, which we, uh, uh, saw in the previous slide as part of the benefits and role of AI as well. And then of course, the, uh, the transformation which a w s has, uh, witnessed as part of the generative AI as well, with a lot of machine learning services, uh, incorporating AI into their DevOps process, making them the market leader in their own segment, and then also the top technology company, right from Google, Facebook, Microsoft. So there are lots of additional use cases which are available, uh, right through editors to Nike, uh, to Disney.

So there are endless companies now which are incorporating it. So it definitely makes it very essential, not just for the individuals, but also for the organization to invite these practices, these skills, as part of their own transformation cycle. So how you can, uh, include these, uh, all these aspects as part of your own transformation.

So I've divided into two segments. So one is the for individuals, for professionals, for practitioners, for developers, for leaders. And the other segment is for the organization overall.

So when we talk about the individuals, continuous learning is very important. I always call myself as a continuous learner, uh, because you need to have that dynamic, uh, roadmap about your upskilling reskilling journey as well, because every technology, every domain is transforming at our lightning speed. So we must need to align our skillset with, uh, that kind of advancement as well.

And that's where the AI skills development, right from Chad, G B T, to, uh, data analysis, predictive analytics, automation skills are really necessary. And also honing your cross-functional skills. So we were talking about T-shaped, uh, professional eha, professional com shaped professionals.

Having that hybrid skills is not just, uh, required, but is really essential. And then also staying ethical and, uh, fostering collaboration and communication is really essential. So there is going to be a lot of, uh, talk around responsible AI as well, because ethical contribution is really necessary to make the overall system really secure, uh, and really qualitative in nature as part of what is being derived, uh, as an output, uh, to the end users or the organization or the overall community.

And now, when we talk about organization, I know it's a bit busy slide, but again, the intent is that you get a lot of guidance references here while I'm talking about different aspects, so that you are able to relate it, uh, more easily. So for our organization, first of all, investing, so getting that buy-in from the leadership is really important in AI tools. Uh, hopefully that investment is not like DevOps tools initially, which was very segmented, very widespread, and then, uh, gradually got consolidated based on, uh, different benefits around different, uh, domain and segments and also cross training.

So that cross-functional skills are already required and aligns with that cross training and data management. So whatever data is also fed in training, the AI model has to be, uh, high quality, high filter data so that the AI driven insights and decision making is also accurate as much as possible. And then cultural transformation change management, which is required for any new technology.

And also setting up metrics and KPIs because it's always required to measure, uh, how you are moving from point A to point B to point C at your desired stage. And that's where continuous learning, continuous training and education is really important. And that's where, uh, practical DevSecOps has been really invested in these, uh, security side of developing that hybrid skills, right from product security to DevSecOps, uh, uh, domain.

So please, uh, do find some time to visit practical DevSecOps whenever, uh, uh, convenient. And then also doing a lot of pilot projects and continuous monitoring and feedback, uh, as part of the organizational transformation. So now when we have seen all the, uh, aspects around generative ai, generative DevOps, hybrid skills for individual organizations, some of the benefits, how it's aligned with it, there are lots of challenges as well.

And what I did was not just mention the challenges, but also share some of the mitigating techniques that might be helpful. Uh, sure enough, these mitigation techniques are not generic in nature. Uh, it'll be more customizable based on different stages of organization, based on their different dimensions, which I will also highlight a bit later in my upcoming slide as well.

So talking about five key dimensions, uh, for any organization transformation journey. When we talk about generative DevOps is not just limited to their technology called ecosystem, but it is also about the human aspects, uh, the process and frameworks, the functional composition, uh, intelligent automation. And if I break it down further to provide you a much more guidance around, if you remember the previous slide from B C G and we, in which we were looking into different segment across the entire value chain or the SS D L C process.

So every domain here is, uh, having different challenges, and that's where every challenge is required, a lot of mitigation, and that's how I have tried to include some of the mitigation along with the challenges. So of course, a lot of challenges included in terms of data privacy and security because AI algorithms are required at access to data raising concerns about data privacy and breaches. And that's where responsible AI plays an important role.

So, uh, the guidance here, which I have tried to provide, is implement a strong data encryption, uh, along with, uh, strong access controls so that the techniques to protect sensitive information is, uh, anonymized so that it is very anonymous in nature. And also conduct regular security audits so that the data protection like, uh, G D P R is uh, intact without any issues. And there are also lots of bias in AI based on how the ai, uh, uh, model is being framed.

So to mitigate that use diverse and representative training data so that you employ fair and bias, uh, detection tools, uh, which also helps in your ongoing audits to identify and correct bias in your AI models. The other is the model reliability. So again, it is something based on how you are training the AI model so regularly validate and test AI models, uh, implement field safe mechanism so that, uh, uh, it provides that human oversight to correct errors whenever needed.

And it also improves the security like in a continuous improvement cycle as well. And then of course, the skills gap like we are talking about, that's the overall objective of this session. So partnering with experts or hiring our talent with AI and DevOps skills while continuously investing in employee training and development programs is really essential.

And then cost and resource allocation, so investment and that buy-in from leadership. So it's always good to do a cost benefit analysis to determine the different AI tools. And you can start small and then, uh, keep scaling it up into the, uh, wider teams and wider departments, just like it has been done in the DevOps transformation as well.

And then also talking about how to integrate complexity in the best way. So seek the help of experts or consultants and ensure compatibility with existing tools and processes so that the disruption is minimum and it is much more productive in nature. And then, of course, uh, on the change management side.

So it's always important that, uh, uh, transparent communication and collaboration in which you involve also the employees in the decision making pro process while providing them training and, uh, supporting them during the trans transition. So that empathy is really a important skill, uh, to imbibe that kind of culture with them. And then in ethical considerations.

So like I was mentioning about responsible ai, so that kind of ethical consideration are really essential. So that, uh, the ethical implications of AI within the organization as well, external to the organization is, uh, really, uh, promoting transparency as well as accurate, uh, decision making and output. And last but not the least, the monitoring and governance.

So always try to implement robust monitoring and governance frameworks to track AI performance and address the issues promptly because, uh, it's also how you are training the AI issues are not promptly being addressed. So like I was mentioning, uh, Aliya, that it definitely requires a holistic approach that combines technology, governance, uh, functional composition process frameworks, human skills, uh, intelligent automation, that five dimensions so that you're able to get, uh, the benefits of AI driven automation and optimization in dev your DevOps process. So if I summarize the importance of the changing landscape of DevOps and the role of hybrid skills and AI in this transformation, so how it really impacts the individuals and organization, uh, in increase efficiency, uh, faster delivery, enhanced quality, optimize resources.

So right from AI powered automation, streamlining to hybrid skills, enabling cross-functional collaboration to AI driven testing and analysis, improving software quality while ai, optimizing resource allocation scaling. Uh, it provides that kind of advancement when you have that kind of gen ai, DevOps hybrid skills. And the other additional benefits is proactive issue resolution, uh, uh, having that kind of continuous learning culture, providing competitive advantage, uh, within your segments, uh, ethical responsibility so that it is becoming more secure system as part of it.

And also future proofing, which I was mentioning earlier, that's also about also getting ready with the next innovation cycle so we are better prepared and aligned how the technology is moving at a very lightning speed. Uh, now. So with that note, it kind of concludes the session for today.

And like I said, uh, it would be, uh, really helpful for you if you would like to have that kind of security based DevSecOps, HandsOn knowledge, uh, imbibe as part of your hybrid skills so that you become more future proof and, uh, career ready. So feel free to visit the website, or if you have any kind of doubts for this session, or you would like to discuss anything specific around generative DevOps hybrid skills or around practical DevSecOps, or how you can, uh, enroll in practical DevSecOps and have that kind of, uh, skill stack built up as part of your own, uh, career and skilling transformation journey, uh, please feel free to reach out to me in any of the coordinates. Uh, I would be more than happy to engage with you.

So with that note, thanks again, hoping that you keep on enjoying the all these sessions at DevOps experience and, uh, take a lot of key takeaways and key learning outcomes as part of it. So thanks again, signing out now.