EP. 17 Modernizing Legacy Systems in the Cloud Era with Ravikrishna Yallapragada

00:12 - Introduction and Career Journey

PJ: Welcome to Cloud Currents podcast where we explore the innovative technologies and strategic approaches driving the future of cloud computing. I'm your host, PJ Farmer. I'm the vice president of product management at TierPoint and my team is responsible for developing and managing TierPoint’s cloud data center doctor and security products. Today we are joined with Ravi Krishna Yalapurgata, the associate vice president of engineering for Google Cloud Partnership at Global Logic. Welcome Ravi.

Ravikrishna: Hi PJ, nice to meet you too.

PJ: He has over 25 years experience in the tech industry. Ravi brings a wealth of knowledge from his roles at industry giants like Microsoft and Nordstrom, as well as his current position at GlobalLogic. Today we'll be diving into topics such as application modernization, AI integration and cloud solutions and the evolving landscape of cloud technologies. Thank you for joining us today Ravi.

Ravikrishna: Thank you.

PJ: So to get started, do you think that you could walk us through your career journey and what first sparked your interest in cloud computing?

Ravikrishna: So I started my journey as a software engineer back india and then I used to develop the engineering products for CAD CAM industry, working at a company called Intrograph. And from there I switched to consulting with Microsoft called Microsoft Consulting Services division. So my goal at that time was mainly to help customers building solutions on Microsoft technology. And then I was very passionate about helping them, especially the large services firms india and then in Asia Pacific region. And from there I moved into several divisions within Microsoft and then finally landed in Redmond, which is the headquarters for Microsoft. So in around after that, when the cloud computing, or maybe just evolving actually as part of Microsoft, we have Azure which is from the beginning of the early stages, I experimented and then I started working on that.

The potential it basically brings is cut down lot of time that especially for developers to provision infrastructure are maybe working on that which takes a lot of time. Cut down all that time actually and give the immediate availability of the resources that they need to do the development of their work. So that's main attraction point, starting with cloud computing. After that, cloud has evolved with a lot of other features. Today it's much more mature and most of the companies are transitioning into cloud, leveraging the cloud capabilities. So that's what one of the things mainly attracted me. So I primarily worked most of my career, 50% of my time I spent in Microsoft. And then after that I moved into Nordstrom primarily to understand.

Nordstrom is known for the best company when it comes to customer service to understand how they do it and what some of the principles, practices it follows. So understood that and it's an e commerce fashion designer company with its own niche addressing the customer needs. I enjoyed working there and after that I moved into different companies, into consulting world and finally landed in global logic. So that's what my high level journey from my career standpoint now.

03:57 - Lessons Learned in Cloud Architecture

PJ: That's great. Thanks for that. And of course, I love Nordstrom and I do agree on that customer service comment that you made in so many ways. So what a great journey. What do you think through some of these experiences that you have? What do you think is the most significant lesson you've learned as you've kind of transitioned to cloud architecture and making things like cloud ready built for the cloud?

Ravikrishna: See one of the key lessons throughout my journey. Washington. You need to be on top of the technology because especially technology is growing so fast nowadays. If you are not able to understand where it is going, what are the key trends in the industry and what are the technologies coming up. And then if you don't ramp up yourself, then it's basically going to difficult for anything that you can add value to your customers or to any business problems. Fundamental thing is you need to always keep running and then you obviously need to invest in yourself. You need to grow yourself and understand where the industry is going and where the business problems the customers are placing and then see that how you can apply the technology.

So that's where you need to always keep it on the eye to make yourself differentiated or maybe a valuable resource to anyone.

PJ: I totally agree with that. I have had that conversation throughout my career. I won't tell you how long I've been, what year I started as an it intern. We'll just keep that to myself. But it was measured in decades and not just years. But I, throughout this journey, have often had my peers and other people I work with talk about training and talk about keeping up to date. And oftentimes those conversations go towards a budget. You know, for the company that you work for, which is great, you should absolutely take advantage of that. But at the end of the day, if you want to be in technology and you want to be able to grow your career, it is absolutely your responsibility to keep current and to stay with it. And there's not necessarily a training program that's going to do that for you.

Like you have to take that responsibility. Don't you agree?

Ravikrishna: Yep, absolutely. That's what I'm saying. It's your personal investment to into your career. So whether a company provides the training or the opportunities or not, it's your responsibility to invest in yourself so that you can add more value to your career.

06:40 - Challenges in Application Modernization

PJ: Yeah, absolutely. Well, so I know through this journey from what a great journey, what a great career, companies you've gotten to work for and what we've got to see you certainly you have seen what some challenges are when they are modernizing their applications for the cloud. Do you think you could share some of the biggest challenges that you've seen with modernizing applications for the cloud?

Ravikrishna: Sure. So when we go into, the main thing is especially when we are going to modernize the application. So there are two aspects to it. One is from a technology standpoint and another one is from culture and then organizational, the way they do the things actually. So you need to look at predominantly the second part is major role, especially getting all the stakeholders aligned. That why we are doing this modernization.

What is the benefit to each division or the departmental organization, how that will transform, help the company to better in a competitive marketplace and other things like that. So it's very important that first we need to get alignment across all the stakeholders and everyone needs to agree that yes, it is a very important thing for the company and then get the buy in from them.

So that's the most of the time that's the biggest challenge. But once you align that implementing with the technology, you will have when it comes to on the technology, the problem is that getting the talent and retaining the talent. So because the cloud is growing so fast and then you need a lot of people to build that expertise and the knowledge. So right now, even if there is a shortage of lot of people talent in that space. So once you have some talent, you need to ensure that you retain the talent and then make sure that they build their skill set and then do that modernization thing, whatever is required. So those are the main two things that I would say from my experience is the biggest challenges in the modern applications.

PJ: That makes sense to me quite a bit as well. And do you think though, I mean those are the two biggest challenges. But in your experience, do you think it's the technology or kind of that organizational, that culture and that organizational change, if you will, that takes more time? Which of the two do you think is usually a bigger challenge?

Ravikrishna: I think the culture, organization change and development and cultural change will take long term thing, but you need to start at some place. So you start small with one division or one department or with one application and then showcasing that as an early adopter or maybe early winter and then take that to the entire organization as a role model and then keep them accelerating it. So that is the only way most of the time from practical standpoint, but at the same time, the senior leadership pushing that the priority and then rewarding people and then encouraging that, giving that appreciation and other things will help people to quickly transition and change their behaviors and then change the culture. Migrating goers more to the modernizing the organization and the applications.

PJ: And listening to you talk about it, I can't help but think about agile development, excuse me, software development. It's an iterative approach that you have to take to organizational change management and to buy in and to bring in people in. And Agile is also an iterative approach to making small changes and making progress towards something and making sure you're on the right path all along the way. So interesting. I feel like those things can go together pretty well.

Ravikrishna: Yeah, absolutely.

PJ: So hey, since we're on it, why don't you share with me an example of a successful application modernization project. And you know, we talked about biggest challenges. I'm wondering if those biggest challenges feed into the key factors why that example was successful.

Ravikrishna: So one of the things we implemented was one of the large applications at one of the company that I mentioned was mainly to replace all the some of the legacy systems, which are multiple system names, mobile critical business functionality, and then replace it with a, modernize that with a unified platform so that it can support in the upcoming integrations with the different vendors and partners and then even support the customers in a better way. So initially when we looked at it's like a lot of six to seven different types of applications which are doing the different to all technologies. So what we decided was to build it on a cloud platform.

And then we started looking at how the customer journey will be integrated all these things and looked at what are the main aspects of the customer journey that we wanted to get. So basically, in other words, you look at what are the core business processes or customer journey that you wanted to give better experience for your end users and then look at how that is being done today in these world, five platforms or six applications, and then started migrating that into a unified platform. So that's what we did. The journey.

I was not able to give all the details in terms of putting the company in because of the confidentiality, but the end result is that were able to take that approach in terms of looking at what are the core customer journeys, touch points to that customer across the different services, and then looking at, okay, how do we modernize that with the different one is from a business process team point perspective and at the same time, how the technology can enable that and then combine those two things. We re architected all those things onto the new platform or at a simple thing. It's like that took that project to even version one to be released to. It took more than a year.

It's like 40 to 50 engineers on the team and then some of the product management team coming from looking from customer experience standpoint and then working together with business stakeholders and prioritizing that. So it's a joint effort from the business stakeholders, the product management and the engineering teams working together to deliver that fun chart.

PJ: Makes sense. I'm curious if in those teams I was listening to some of the things that you were doing and did you have obviously software developers and product managers, what are maybe some of the other roles that might have been on that team that may not be obvious to people? Did you have business analysts or other roles like that could help to define those business processes and really help those out for the software developers?

Ravikrishna: Yeah. So one of the things, main things is if you look at different roles, one is the business stakeholders who are the business and then who makes basically understanding of what is the priority for the from the market and all things. Then the marketing teams, which is going to run the campaigns and then understand what users and end users are requiring. And then the third one is UX in Geneva. So maybe we'll look into from the product experience standpoint, customer experience standpoint and doing the customer research, and then doing some prototyping and then conducting user research workshops and other things to finalize that, which one is going to work better for them.

And then you will have the product management which is going to look at what the trade offs in the product features that we need to take and what is the value proposition for the ROI for implementing those features. Then it comes to the engineering teams and it comes basically some other things where we have like product owners, which is going to be individual feature owners, who is going to understand that feature and then implement, then architects who is going to design looking at the entire system from a technology architecture perspective, and then the engineers who is a full stack engineer. So he's going to code and develop and test it, those features. And then finally the release manager who is going to be rolling out all these features in a checkpoint, cadence tool production and go like those type of things.

So these are the main roles that typically we follow these particular type of cloud campaign.

PJ: And Ronnie, a lot of projects, it sounds like they are on customer and client facing products versus internal products that are internal to a company or an enterprise that they use. Have you worked on both types of those efforts or is it mainly client?

Ravikrishna: Yeah, no, no. We work on both type of things, but predominantly majority of the things we face on customer side, but sometimes we also internally work on that because internal, our own organization and company transformation needs to happen. So we'll have even internal application modernization and all things.

PJ: Sure, sure. Do you find that the internal application modernizations are of those any less or more challenging than modernizing a client facing application? I'm just curious.

Ravikrishna: From a process standpoint, both will be the same, but when it is a customer thing, then you will have more stakeholders and then the risk and then the reward will be very high because money is having direct visibility and then company's credibility is going to put in front of them in the implementation thing. So when it comes to internal things, sometimes it could be an experimental type of thing. People will say, hey, try that and then we'll kill a pass type of an approach and other things. But in customer scenarios you will not get that type of an opportunity. So that's the main difference.

17:13 - AI Integration in Cloud Solutions

PJ: Yeah, very good. Well, hey, I have to bring it up because everybody's talking about it everywhere. And that is AI. You probably could have guessed that one. And so I am curious, how are you seeing companies leverage AI and generate AI in their cloud solutions?

Ravikrishna: Okay, so like the main thing that what we look at when it comes to AI is from two types of things. One is called predictive AI, and then second one is recently getting popular in the generative AI. So predictive AI, like everyone knows that it is going to do the given some information, it is going to predict whether something is going to happen or not, something can be done or not. Whereas the generative AI, it is basically you take the existing information and try to generate new information. That's why it's called generative AI. You generate something out of the existing thing and that's what is called this year that.

So two different things.

So looking at your problem and looking at what you're trying to solve, you need to pick and choose whether you wanted to go with the predictive, a type of solution or maybe you are going to look at the generative AI. But the companies are maturing the generative AI also too much so that they both can work together to provide a holistic solution. And that's where the organizations and whoever is providing those capabilities, maturing those areas actually makes sense.

PJ: So once they decide predictive or generative, can you give me any examples of how they're actually leveraging it in cloud solutions that have been interesting that you might want to share.

Ravikrishna: Yeah. So let's take one example, like for example in case of predictive AI, suppose say I wanted to find out whether what is my, given my continuous sales right now for every quarter, how much we can make in the next couple of quarters in this particular fiscal year. That could be one use case. So what happens in these type of scenarios? Are you predicting how the sales will be in the future? So you take all your past data for the few quarters or maybe few years of data, how your organization is doing, and then use that model as the starting point to fine tune later with see that how that is performing versus what it is predicting and what exactly happened in the past and try to narrow down the model is going to give the eye accuracy and other parameters.

And then once you have done that, now you take that model and put it in production and see that now how you predict how in the future quarters are going to come. And based on that, you adjust your resources and then evaluate whether what you're forecasted versus what you're actually seeing is matching or not. So that's how you will iterate the model and then improve the performance of the model that is in the predictive side. But when it comes to the generative side generating ASI, most of the people are using it for content generation, which is basically especially in marketing side. A lot of people are using it for marketing campaigns or maybe generating new image content or maybe blog content or maybe some of these things. So that's where generative a is playing a predominant role.

Ravikrishna: And at the same time you are also doing the generating the sentiment analysis type of thing. But it's predominantly how you generate, given something a test whether can you generate a new version of the text. Maybe people are using transforming it in different industries, including Nick Cleaner, within the resume and in the recruiting world where people are looking at hey, I can give you a job description and then can you tailor my resume to match that job description? One is one of the best news cases so that you can be directly get the interview call from the ATS systems passing that and then you can get that one. So that's another use case where you can mix and match different type of scenarios. Like one is related to images and I'm giving you image.

Can you create a new image based on the information that I given in this one? Typically with the technical term is called prompt. So you give you a prompt that what you want to achieve and then it is going to generate based on that prompt to the content. So that's the main thing that nowadays a lot of people are doing as per generation.

PJ FARMER: I'm curious, when it comes to developing and cloud solutions, have you seen anyone use generative AI to create code to help build things for their companies and enterprises? Have you had any experience seeing people do that or working with that?

Ravikrishna Yallapragada: So that's one of the use cases which we call it as SDLC software development lifecycle use case where you collect the requirement and then from requirement, the typical process is you collect the requirement, then do the design, then do the development and testing and everything. So how generated is AI is helping in each one of these areas is like the moment you get requirement. From that can you build the user stories from that can you build the test cases for that?... And then from that you can generate the code in a particular language like Python or Java, given those roms, and helping developers to quickly come up with the prototyping code and then later enhancing that to the production ready level code. But the initial boiler template code and everything in the entire life cycle can be generated through a generate way.

That's what is most of the time that people are seeing that at least 10% improvement in the developers. If you are following the entire SDLC lifecycle.

PJ FARMER: Oh, that's great. I know that I personally use generative AI in that content creation when it's not proprietary, not sensitive information that I'm using, even as easy as I write something and then I have it clean it up, right? And it's such a huge time saver, and I can see how it would be a huge time saver and coding and through the whole SDLC lifecycle like you're talking about. But of course, with that comes security and ethical considerations and things along those lines. And I'm curious to hear from you, what are some of the security and ethical considerations that you're thinking about when implementing AI in cloud environments?

Ravikrishna: So typically that is the area where when initially generated AI came. Everyone's concern is that legal and the security areas still it is evolving, but it's getting mature. But most of the time the companies agreed, or maybe some of the concept called responsible AI, where a set of principles have been established across everyone and most of the companies are following that. So the way they are implementing some of the cloud providers is basically whenever you are generating the content, go through a list of filters, which is going to be looking at whether is it safe, is it addressing the ethical aspects of it, whether it's legally viable or not and passing through all those filters, eliminating the content that doesn't match and then finally serving that content.

So there are provisions in the tools that to use that one and some things to customize even there is what everyone is looking at. So that's what is the responsibility. AI is one of the categories that everyone is agreeing and that everyone has to follow. Otherwise then some of these things can be violated. So that's what right now that's an area growing. Everyone is investing heavily. And then I'm sure that in the next coming few years I think it will get matured much more thing that's already useful for everyone.

PJ FARMER: For sure. With responsible AI, are there like other security considerations that you can think of? I've heard of AI hallucinating, if you will, and things along those lines. How do you prevent, or what are some of the ways you might have seen to prevent that access to maybe the language model or whatever, so that it doesn't hallucinate later or any of those other sort of anomalous results that you might get out of it.

Ravikrishna Yallapragada: So some of the companies, the way we are seeing or maybe approaching that one is you take a generic model and then customize it to your data and proprietary thing, then put those controls in place so that you're confident that you're not giving the full control to the public models and everything. So that way you can narrow down that whatever may be the responses you're getting is basically controlled within that context. So that's the one of the things that some of the process people call it as grounding and then using that one, they are trying to customize it to their needs and so that they can own the data when it comes to legal. And some of those issues actually makes sense.

PJ FARMER: So, you know, people talk about AI being accessible and being democratized, if you will. Right. And so can you discuss the role of cloud platforms in democratizing, excuse me, access to AI and machine learning technologies?

Ravikrishna Yallapragada: In my view, actually it is up to the cloud providers to give in the platform and the foundational capabilities.... And how do you democratize and some other things use it depends on the individual ecobool applications on top of that. That's my point of view, because it's not very practical to say that how to democratize something across all the scenarios and maybe across to all stakeholders from the top parties, I think they will give you the foundational capabilities and then the individual users who is going to implement on top of that needs to take care of how we are going to democratize some of those things to meet your needs makes sense.

27:59 - Enhancing Customer Experiences with Cloud Technologies

PJ FARMER: So, you know, people talk about AI being accessible and being democratized, if you will. Right. And so can you discuss the role of cloud platforms in democratizing, excuse me, access to AI and machine learning technologies?

Ravikrishna Yallapragada: In my view, actually it is up to the cloud providers to give in the platform and the foundational capabilities.... And how do you democratize and some other things use it depends on the individual ecobool applications on top of that. That's my point of view, because it's not very practical to say that how to democratize something across all the scenarios and maybe across to all stakeholders from the top parties, I think they will give you the foundational capabilities and then the individual users who is going to implement on top of that needs to take care of how we are going to democratize some of those things to meet your needs makes sense.

PJ FARMER: We talked about AI quite a bit there. I want to flip back to something you said earlier about customer experience at Nordstrom and others, and I know you worked on their loyalty platform at Nordstrom. I want to ask you, how do you see cloud technologies enhancing customer experiences in retail and in other industries?

Ravikrishna Yallapragada: So there are multiple ways you can do a lot of things in retail and the e commerce world with the AI. Like for example, one of the things that coming from that background, I can say the first thing is recommendations like okay, you bought something from the store, then maybe looking at your personal preferences, what type of person you are, then giving a personalized recommendation of what other products can be interested to you is one of the use case. Then on the company side you will be having a lot of maybe sometimes millions of products or maybe millions or maybe under thousands of products actually. How do you categorize them into a different categories and other things through AI.

Not going through the manual laborious process is one of the best efficient ways of looking at how you can leverage the AI in that space. Right.

And some similarly, there are some enhanced use cases which some of the people implemented, which we can see that is basically even in when I was working, there is basically the moment you enter into the store,... identifying that you enter into the store and then navigating you to the latest products on the shelf or based on your preferences could be one of the other use cases that where people liked it more because then they don't need to search for the entire store and then save time for them because the stores understand you and then the company understand your preferences, what you like and what you like based on cost purchase and then your preferences and then what new merchandise is arriving based on the season and all these, they can direct you directly there because they know that you are entering into the store.

So giving you at that time real time notification and then directing you to the right product and is benefit for both to you and to the company.

PJ FARMER: Absolutely, absolutely, definitely enjoyed that myself. Honestly, it saves me time and gets me what I want. I like it. I mean, I love that. Right. I'm a big fan and advocate of saving time and getting what I want faster in all aspects of my life, not just shopping, but pretty much everywhere. But how do we, you know, how do you balance that need for that data processing and collection of data and doing that in real time for that customer experience with data privacy concerns, of course, that seem to come up all the time. What do you think how do you balance that?

Ravikrishna Yallapragada: So basically when we are looking at this the way right now, the data is exploding everywhere. It's like every company is getting too much data. And then, so that's where, when you are really looking at AI and looking at leveraging that, the first thing you need to do is having a foundational data platform which is going to do basically the job of the data platform is that you will take all the information that is coming through different channels. It could be stores, it could be your web, or it could be from your devices and from your customer call centers.

And I think take all that information and then you stage it through different levels and then build that on that and clean the data and then make sure that at different stages, right, stakeholders are only giving the access to the data so that they can make the decisions. So that's the process that you need to do as a foundational activity,... building a data platform and then already to that extent, even building a lake house is the first fundamental step so that then you can look at the real time information access and getting the real time insights from that one makes sense.

32:21 - Multicloud and Hybrid Cloud Approaches

PJ FARMER: So certainly with some of your clients and some of the places that you've worked, you've had to determine if you're keeping everything in one cloud, in multiple clouds, in some sort of private cloud versus public cloud, hybrid cloud scenario. So when you're thinking about that sort of design, what are some of the factors that come into the approach? Like how do companies decide? Is it multi cloud, is it hybrid cloud? What approach do I need to take in my application development?

Ravikrishna Yallapragada: So the first thing is, I would say, being a consulting answer, it depends. So some of the things like your data, like I'll give you. So I'll classify this into two patterns actually. One is called multi cloud path, where basically you're going to use multiple clouds to meet your needs, versus the hybrid cloud, which is basically some on premise your own data center, and then that needs to be integrated with that cloud. So typically if it is some other things, what we are seeing is basically there are any decisions from a regulation or maybe from the government that we need to follow certain things like healthcare data or maybe some other regulated data, then it has to stay where the data is originated.

Then most of the time then you'll go for an hybrid type of approach where you retain the data on your own data centers, provide your own controls, and then extend that to the public cloud where nonsensical data can be processed and all beings in the flow, that you will do that. But there is other pattern where when most of the customers also know exploring the multi cloud thing because then one size doesn't fit all your needs type of an approach where some companies,... they wanted to have the flexibility of leveraging the best in all the clouds, so they might implement some features in one cloud and then some of the other best features in another cloud, and then they want to do the integration. And then there's one scenario, and second scenario is I don't want to get locked in with one flow provider.

And then I wanted to have the ability for me to have that flexibility is another driver for them to approaching the multicloud thing. So these are the some of the design considerations you will take looking at what your business drivers are, then determine which approach is the best for that particular company.

PJ FARMER: That makes sense. I think I heard you say from hybrid cloud, a great reason to do that is if you've got regulations or concerns where you need to keep some of the data in your own data center and then connect those things out. And then for multi cloud, I really liked what you said there. And that some people do it to get the best of each cloud, the best capabilities of each cloud, and some do it just so they're not locked in. And I'm curious about that last part. When I think about multi cloud, both of those are really good reasons to do it. But man, that's some complexity, right? I'm thinking that you might need software developers with different skill sets and different clouds. When you do that, how can an organization effectively manage that complexity that comes with multiple cloud environments? What do you think?

Ravikrishna Yallapragada: So it depends on what type of organization the organization is like into software, having their own department of it, and then they have engineers working in that. So some of the companies, the way we look at is, hey, we will build expertise one cloud, but then we'll partner with other companies and then we have other companies who is experience in other cloud and then do a co development with those companies actually. So then after over a period of time, if you think even that is required,... then you observe either that company for acquisition or maybe you build that capability in house again. So that way you will have multiple cloud expertise within the organization. It all depends on which direction which brought to the rear business strategy.

And then what is your technology strategy is driving based on that you will build that competence and capability within those organizations.

PJ FARMER: Yeah, Dan, makes sense. Do you find that oftentimes wherever. I mean, it would make sense to me that an organization's capabilities in a specific cloud, they would lead with that, right. They would go there first, they would start there and then try to maybe duplicate some of that into another cloud, if you will start there with their business processes and work through it that way with what they're familiar with before going to other clouds. Do you see that happening or does it happen differently, you think?

Ravikrishna Yallapragada: Yeah, I happen sometimes. Most of the time people will say, hey, let me optimize my business process and at the same time build that capable tending element and see how that is going. It's not like a waterfall approach where hey, let me first clean up all my business processes and then I'll go and do that. So that's the long run. And it doesn't work in this particular because it's, things are moving so fast. So, but people are going to do both simultaneously and then enhance that incrementally. You improve your business process and at the same time try to implement some of the technology things I'm ready to.

PJ FARMER: So let me ask one last question on this route. What are some of the tools or practices that you would recommend for maintaining consistency across the different cloud platforms?

Ravikrishna Yallapragada: So there are again two approaches. So we follow two things. One is each cloud providers provides a tool set to manage some of their things in the best way. But there are other things. Sometimes we'll follow the other partner products or maybe some other products,... which is going to provide agnostic hackers on clouds. So it depends again, your approach to what do you want to do in terms of like some companies are, they wanted to go and use the cloud native tools to manage the entire end to end process, but some of the customers are preferring, hey, I have all clouds, but I don't want to have multiple people looking into that. Is there any way that I can have a separate tool which can sit on top of all these things and give me a high-level view of what has been happening? So it's basically mix and match type of scenario that we are looking at.

39:15 - Keeping Teams Updated on Cloud Technologies

PJ FARMER: Makes sense. Earlier we talked about how you have to take the responsibility for keeping up with technology, you know, following the trends and learning and just being, always knowing new things. Right. And using new things. Since you're a leader in the cloud space, like what are some of the strategies that you use to keep your team updated on those rapidly evolving technologies? How do you maybe steer them in directions that they need to go to help support what you guys are doing?

Ravikrishna Yallapragada: So basically what we have is we have partnerships with the, all the cloud providers. So, and then we run different training programs for the people depending upon what area they wanted to enhance their skill set. And then also we encourage people to go and take the certification programs and then some of them, we sponsor it and some of them will do motivation, make we reimburse their certification vouchers and things like that. And then we encourage them to participate into the conferences, we send them to conferences and then other events so that they get exposure of what has been happening in the industry and then invest in themselves to learn new technologies so different, follow and encourage people to take on those opportunities.

PJ FARMER: Great plan. Love it. So let's look ahead.... Let's look ahead here and I would love to hear from you in terms of what are some of the emerging cloud trends that you're excited about and tell me why.

41:06 - Future Trends and Preparing for the Future of Cloud Computing

Ravikrishna Yallapragada: So the cloud trend is mainly, the generator is the main thing right now. Everyone is trending towards. And the second area I would say is the overall mission, learning the AI, the space that is growing very fast. But at the same time, to make that successful, I would say that the data analytics is one of the big trends, actually. So you get the real time insights and real time predictions from looking at the massive data sets and then quickly identifying what information you need and surfacing that to all the decision makers is one of the key things for anyone to succeed in this particular digital age. So I would say basically data analytics, artificial intelligence, and then especially generative, these are the three things. And then fourth one is the cyber security because, which cuts across all these things. So these are the four areas, from my perspective, is going to grow very fast in the coming years.

PJ FARMER: Yes. Security is not going away anytime soon for sure. Information security and. Yeah, absolutely. Data analytics feeding into everything we're doing with AI, you gotta have it, so. Absolutely. And it's exciting, it's fun. I know having done this for a few years, this is definitely a pivotal time where we're all getting excited to learn something new and that we know is going to really change the way people approach life, even how they work and how they live and how we do get things done. So. All right, so last question. How do you envision the future of cloud computing over the next five to ten years? And what should companies be doing right now to prepare for the next five to ten years?

Ravikrishna Yallapragada: I think from my perspective, most of the companies are now recognizing that cloud is a new way. Basically, if you wanted to really be competing with your competitors and being in the marketplace and then want to grow your business, then the best thing is for everyone realize is the cloud. So most of the companies are now tapping into cloud and then building their infrastructure and everything. Moving into cloud is the first thing that everyone is trying to do. The second thing is it doesn't stop there. Mainly because with the amount of growth that is coming from, especially from AI and mission learning areas, basically, yes, now we moved into cloud, now we have capability to scale and grow, but unless you serve all the right information at the right time to make the decisions that you'll be again behind.

So most of the companies that we are now looking at, who already took the first step, looking at, okay, now, how can I make the real time decisions in the shortest time frame? And making sure those are the right decisions based on the data is the second critical thing I think is going to lead the way.

PJ FARMER: Makes sense to me. Well, hey, I just want to say thank you so much for spending time with me this afternoon and talking about cloud and AI and trends. It's all top of mind for all of us these days as we work through new strategies and things to accelerate our business outcomes. Right. So, good stuff. Very nice to meet you. Thank you.

Ravikrishna Yallapragada: I felt the same. It's nice having a good conversation with you, PJ, and I hope we have a good day.

PJ FARMER: You too, sir. Thank you.