Trending December 2023 # Exclusive Interview With Rajamanohar Somasundaram, Founder & Ceo Aquaconnect # Suggested January 2024 # Top 19 Popular

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Aquaconnect is a technology-driven full-stack aquaculture input and outputs platform with embedded fintech, supported by a phygital distribution network. The startup was founded with an aim to improve farmer livelihoods and promote sustainable aquafarming among the farming communities. Analytics Insight has engaged in an exclusive interview with Rajamanohar Somasundaram, Founder & CEO of Aquaconnect.

1. Kindly brief us about the company, its specialization, and the services that your company offers. 2. Mention some of the awards, achievements, recognitions, and clients’ feedback that you feel are notable and valuable for the company.

Aquaconnect has been recognized as the ‘Best Agri Startup in Application of Digital Technologies Award’ & the ‘Most Innovative AgTech Start-up’ by FICCI. Aquaconnect has also been featured on the ‘Forbes Asia 100 to watch’ and ‘World-Changing Ideas 2023’ by Fast Company. The start-up is part of the Innovator cohort at Seafood Innovation Project and Hatch Accelerator, Norwegian Seafood Innovation Cluster. Aquaconnect is a platinum winner of the Agriculture insuretech award by the World Bank Group. The startup was selected for the fifth edition of Google for Startups Accelerator (GFS) India.

3. Kindly mention some of the major challenges the company has faced till now.

The industry, as dynamic as it is, gives the opportunity to work with diverse groups which have their own set of challenges. At first, Aquaconnect was facing difficulty tailoring its service to many markets due to the country’s diverse languages and thought processes. Even though the industry is 1000s of years old, there are still many apprehensions attached to it and the learning curve is steep along with the low awareness of the technology-driven solutions. Inculcating knowledge of data-driven farming in the farmers was another challenge, from traditional aqua farming to data-driven farming, it was a journey in itself. In the last 5 years, Aquaconnect has overcome the challenges by providing multi-lingual support, launching awareness programs for the farmers, and helping them comprehend the solutions and how it benefits them. Along with these, Aquaconnect has a strong presence of an on-ground team in all the cities with major operations. The team gives handholding support & better last-mile connectivity to farmers & other stakeholders.

4. How do you plan to revolutionize the Indian market and what are your plans to tap the market? 5. How do you see the company and the industry in the future ahead?

India is the largest exporter of frozen shrimp in the world and the second-largest aquaculture producer behind China. In fact, aquaculture is one of the most sustainable protein production value chains in the world. With schemes like PMMSY and the proposed reduction in import duties on certain aquaculture-related input products announced in the budget, we see immense potential for growth of the sector, overall. An interesting point to note is that while several industries were battling the brunt of a global pandemic, India exported worth $8 Billion seafood during the last financial year amidst mounting challenges. These numbers are a strong indication of the ‘sea-change’ that the sector is poised to witness in the coming years. Further, with the rise of technology-driven solutions, and species diversification on the anvil, we expect the coming years to be good for the Indian aquaculture industry. Other factors like increased global demand, the thrust provided by the government’s new schemes, and a good leap in value-addition in seafood processing will play a pivotal role in achieving the ambitious target of doubling seafood production. The aquaculture industry is growing at a double-digit rate and is a sunrise sector, there is a huge opportunity for us to assist more and more farmers. We look forward to meaningfully contributing to India’s food security through innovation and tech and becoming the leading, full-stack aquaculture platform in India.

6. What are some of the challenges faced by the industry today?

Financing is one of the major challenges in the aquaculture value chain. Banks stay away from adding tremendous value to aquaculture farming because they are unable to come up with risk assessment and mitigation strategies since everything is happening underwater. We are working towards mitigating this risk factor through tech. Our boots on the ground and eyes in the sky model is addressing the issue by providing the correct insights to the financial institutions. These insights further provide underwriting capabilities for the bankers and financial institutions to lend credit products to the farmers. We are also partnering with some Fintech startups and prominent banks to enable access to low-interest capital for aquaculture farmers and other stakeholders in the value chain.

7. What is your Leadership Mantra?

First and foremost, delegating the right work to the right people is important as everyone has an area of expertise. I believe in creating a flexible environment that enables the team to perform its best. Last but not the least, infusing trust and courage in the team to go the extra mile and experiment with newer possibilities is of utmost importance as it motivates them to think out of the box and achieve greater heights.

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Exclusive Interview With Vid Jain, Ceo & Founder, Wallaroo

1. Kindly brief us about the company, its specialization, and the services that your company offers.

Wallaroo Labs is an MLOps platform focused on the last mile of machine learning (ML) implementation, i.e., getting ML into a production environment. By saying the last mile, we mean more the part of deploying an already built model. We make testing, deploying, running, managing, and observing these models in production easy and highly performant. We’re privately held and backed by several leading venture capital firms including Microsoft’s M12, Boldstart Ventures, and other leading enterprise software VCs.

2. With what mission and objectives, the company was set up? In short, tell us about your journey since the inception of the company.

We started in 2023 as a group of engineers dedicated to solving the increasingly common problem of analyzing large amounts of data via computational algorithms efficiently and at scale. By applying our expertise in building distributed computing systems in industries such as high-frequency financial trading and AdTech, we built a high-performance compute engine like nothing else on the market. While our customers could now efficiently analyze their data and use it to run ML models at scale, they had one biggest challenge: bringing those models online easily and understanding how they were performing to generate business value sustainably. Like most organizations, they followed the standard DevOps playbook: data scientists would build ML models to solve a business problem, and engineers would launch them using a patchwork of open-source software and containerized model approaches. However, they found that the standard DevOps playbook just didn’t work for ML. Models often had to be painstakingly re-engineered, meaning the deployment software couldn’t process data fast enough — even when running on an alarming amount of computing resources — and it was unnecessarily difficult to see how models were performing to measure their ongoing accuracy. We started Wallaroo with the mission of making it the easiest, fastest, and best-performing way for enterprises to generate sustainable business value from their AI/ML programs.

3. Brief us about the proactive Founder/CEO of the company and his/her contributions to the company and the industry.

Vid Jain comes from an academic background, with a Ph.D. in Theoretical Physics from UC Berkeley University. He helped build the algorithmic trading desk at Merrill Lynch, where he first stumbled upon the “P&L of ML” concept. That is, this is where he noticed that the return on investment into your AI and ML program wasn’t from data wrangling or building models in a lab – but from having those models in production, driving results. As those models can expire quickly as the markets changed, you need an easy way to continually monitor their ongoing performance and make it easy to deploy, test, and undeploy models.

4. Tell us how your company is contributing to the IoT / AI / Big Data Analytics / Robotics / Self-Driving Vehicles / Cloud Computing industry of the nation and how the company is benefiting the clients.

Data scientists are building better and more complex models in the lab, but the lab environment is very different from having a model running at the edge. For example, you can spin up cloud computing to train and run a model on a large quantity of batch data in the lab. But when you have that model deployed at the edge, for example, a computer vision model trained to detect pedestrians requires you to run that model with ultra-low latency at the edge, where millisecond latency matters. This means there is very limited computing available to run the model at the edge. Our purpose-built engine makes it possible to still run that model at high performance using much less computing. There is also the issue of model testing and observability. How do you test different models across a fleet of smart devices? Most conventional MLOps solutions don’t make testing and managing model versions across hundreds or thousands of devices easy. We think of ourselves as an “ML control center,” giving you easy visibility and management of all your models in production and testing on the cloud, on-premise, or at the edge.

5. Kindly share your point of view on the current scenario of Big Data Analytics and its future.

At the beginning of the Big Data gold rush, the promise was that data would be this incredibly valuable asset, so the important thing was to hoard as much as possible and figure out how to use it later. Digitization was generating more data than ever, so enterprises thought if they could just get a handle on this stream of direct customer data, it would fix everything. So, cloud technologies came along and other SaaS providers made data engineering easy and cheap. But data on its own doesn’t do much. Data science became so hot that you could take all this data from various sources and find predictive patterns in that data. Voila! AI and ML models!! This is where I think most enterprises are now. Even the digital laggards are hiring data scientists and building models for different parts of the business. But they’re still not generating a return on their investment. What’s missing? Data scientists are great at looking at business problems and building models, but that’s not necessarily the same as making production-ready code. So, you have your AI leaders, maybe the top 5% of companies, that have figured out how to take these AI models built in R&D and then take them into production to generate value. But you now have these early and late adopters coming up behind them who may not have the resources or ability to hire an army of ML engineers to get these models from R&D into production. We’re very excited about this next huge wave of enterprises that have made the investments and now are ready to make AI an integral part of their operations if they can figure out how to make a scalable, agile AI program.

6. Please brief us about the products/services/solutions you provide to your customers and how they get value out of it.

Machine learning models take a lot of time and huge swathes of data to yield results. A typical machine learning cycle consumes most of the resources for data gathering and wrangling, which may or may not be responsible for generating business value eventually. It is only the last mile execution, i.e., deploying an ML model into the market and observing them for necessary changes in accordance with changing market, that helps make ML models profitable. Having observed this trend, Wallaroo Labs, an MLOps platform enables companies to leverage the power of live data to deploy their ML models successfully. Analytics Insight has engaged in an exclusive interview with Vid Jain, CEO & Founder, of Wallaroo Labs Wallaroo Labs is an MLOps platform focused on the last mile of machine learning (ML) implementation, i.e., getting ML into a production environment. By saying the last mile, we mean more the part of deploying an already built model. We make testing, deploying, running, managing, and observing these models in production easy and highly performant. We’re privately held and backed by several leading venture capital firms including Microsoft’s M12, Boldstart Ventures, and other leading enterprise software chúng tôi started in 2023 as a group of engineers dedicated to solving the increasingly common problem of analyzing large amounts of data via computational algorithms efficiently and at scale. By applying our expertise in building distributed computing systems in industries such as high-frequency financial trading and AdTech, we built a high-performance compute engine like nothing else on the market. While our customers could now efficiently analyze their data and use it to run ML models at scale, they had one biggest challenge: bringing those models online easily and understanding how they were performing to generate business value sustainably. Like most organizations, they followed the standard DevOps playbook: data scientists would build ML models to solve a business problem, and engineers would launch them using a patchwork of open-source software and containerized model approaches. However, they found that the standard DevOps playbook just didn’t work for ML. Models often had to be painstakingly re-engineered, meaning the deployment software couldn’t process data fast enough — even when running on an alarming amount of computing resources — and it was unnecessarily difficult to see how models were performing to measure their ongoing accuracy. We started Wallaroo with the mission of making it the easiest, fastest, and best-performing way for enterprises to generate sustainable business value from their AI/ML chúng tôi Jain comes from an academic background, with a Ph.D. in Theoretical Physics from UC Berkeley University. He helped build the algorithmic trading desk at Merrill Lynch, where he first stumbled upon the “P&L of ML” concept. That is, this is where he noticed that the return on investment into your AI and ML program wasn’t from data wrangling or building models in a lab – but from having those models in production, driving results. As those models can expire quickly as the markets changed, you need an easy way to continually monitor their ongoing performance and make it easy to deploy, test, and undeploy chúng tôi scientists are building better and more complex models in the lab, but the lab environment is very different from having a model running at the edge. For example, you can spin up cloud computing to train and run a model on a large quantity of batch data in the lab. But when you have that model deployed at the edge, for example, a computer vision model trained to detect pedestrians requires you to run that model with ultra-low latency at the edge, where millisecond latency matters. This means there is very limited computing available to run the model at the edge. Our purpose-built engine makes it possible to still run that model at high performance using much less computing. There is also the issue of model testing and observability. How do you test different models across a fleet of smart devices? Most conventional MLOps solutions don’t make testing and managing model versions across hundreds or thousands of devices easy. We think of ourselves as an “ML control center,” giving you easy visibility and management of all your models in production and testing on the cloud, on-premise, or at the chúng tôi the beginning of the Big Data gold rush, the promise was that data would be this incredibly valuable asset, so the important thing was to hoard as much as possible and figure out how to use it later. Digitization was generating more data than ever, so enterprises thought if they could just get a handle on this stream of direct customer data, it would fix everything. So, cloud technologies came along and other SaaS providers made data engineering easy and cheap. But data on its own doesn’t do much. Data science became so hot that you could take all this data from various sources and find predictive patterns in that data. Voila! AI and ML models!! This is where I think most enterprises are now. Even the digital laggards are hiring data scientists and building models for different parts of the business. But they’re still not generating a return on their investment. What’s missing? Data scientists are great at looking at business problems and building models, but that’s not necessarily the same as making production-ready code. So, you have your AI leaders, maybe the top 5% of companies, that have figured out how to take these AI models built in R&D and then take them into production to generate value. But you now have these early and late adopters coming up behind them who may not have the resources or ability to hire an army of ML engineers to get these models from R&D into production. We’re very excited about this next huge wave of enterprises that have made the investments and now are ready to make AI an integral part of their operations if they can figure out how to make a scalable, agile AI chúng tôi product has three main components: a self-service toolkit for easy model deployment and management; a distributed compute engine purpose-built for ML to infer faster, using less compute; and observability, insights, and dashboards to monitor the ongoing performance of models in testing and production. Together these three components make applied AI programs scalable in the enterprise by allowing you to deploy and undeploy models in seconds without major reengineering, making it economically feasible to run these models (often, compute costs can be greater than gains from ML models if not using a specialized engine), and making it simple to test and monitor for data and model drift.

Exclusive Interview With Apu Pavithran, Founder And Ceo Of Hexnode

Kindly brief us about the company, its specialization, and the services that your company offers.

Mitsogo Inc. is a leading provider of endpoint management and security solutions. Headquartered in San Fransisco, California the company has offices in Australia, Germany and a large presence in India. Our customers include SMBs to Fortune 500s, enterprises of all sizes have leveraged Mitsogo’s prowess in device management to drive business productivity and compliance. Our solutions adapt to the most complex of business environments. Our company has been helping organizations in over 100 countries to stay agile and competitive in an increasingly mobile world.

With what mission and objectives, the company was set up? In short, tell us about your journey since the inception of the company?

It all started with a trio of friends at a coffee house. We were all IT professionals hovering over an idea to start something of our own. One fine evening, we decided it was time to actually do something about it. I was the first to quit my job and soon my friends followed suit. It was at an IT conference in the US that we zeroed in on endpoint management.

Kindly share your point of view on the current scenario of big data analytics and its future.

I have high hopes for big data analytics. For the past 5 years, every decision that we made at Mitsogo was after data-centric research. In every industry, there are large amounts of uninterpreted data, which if properly analyzed can help increase their productivity and revenue by at least 25%. Even then, the interesting fact is that there is still a long way to go. It is an industry with tons of potential. With data being the center of every decision being made and more businesses bringing on data analytics professionals onboard, the next five years look very promising.

Kindly mention some of the major challenges the company has faced till now.

When we founded Mitsogo in 2013, it was me, and my closest friends from work sitting in a small room working on an idea that we thought could revolutionize the enterprise device management market. Fast forward to today we are a 250 people company with 5 offices in 4 countries and doing business with clients from over 120 countries. Even though it was an amazing journey, it wasn’t always smooth sailing. In 2013 Software as a Service (SaaS) was a relatively new concept and businesses were just starting to familiarize themselves with the subscription model for their business tools. We had to face several challenges along the way. But the biggest of them all was when the pandemic hit. It was a time of great uncertainty. Everyone was worried about their safety, and slowly work slipped into second gear. For me this meant that the team needed support more than ever, I had to switch through the roles of the captain, cheerleader, and even at times the therapist. We put employee wellbeing first and immediately established a fully remote work landscape. But it didn’t work well as our critical teams had a lot of dependency on office resources. So, we made a decision that made absolutely no sense from a financial standpoint. We rented out hotel rooms and made them our office guesthouses for e our employees around the globe, ran security checks and screening protocols equivalent to that of hospitals. We made tweaks to the insurance such that if anyone got tested positive, it was easy on their wallets. In a couple of months, the stress levels started to climb down, the team started to enjoy their workcation and slowly productivity became better than pre-COVID levels.

What is your biggest USP that diﬀerentiates the company from competitors? Please brief us about the products/services/solutions you provide to your customers and how do they get value out of it

Hexnode UEM simplifies the work of an IT admin.

When an organization deploys devices for work to its employees, they also need to monitor and manage the devices. The IT team should have complete visibility into all the company’s devices and where they are, what’s in it etc. This is crucial because if one device gets compromised or falls into the wrong hands it will put the whole company at risk. So Hexnode gives IT the power to grant or revoke access to the work resources or data and even wipe the devices remotely if needed.

Every industry needs an agile UEM solution for effective management. Our key markets are based in North America and Europe and our customers are spread all across the world from various industries like government, aviation, transportation, education, hospitality, healthcare, logistics, human resources, retail, non-profits, manufacturing, and construction, etc. You can find the customer stories on our website. To name a few we have Oasis, DKT, Conde Nast, Hampton Jitney, Nathealth, Checkup, Wattbike, Eatstreet, etc.

How do you see the company and the industry in the future ahead? What’s your growth plan for the next 12 months?

Growing in a post-covid world has its challenges, but we managed it successfully with the support of a committed team. We recently opened a second office in Chennai – an even bigger office, housing around 500 employees. We plan to hire freshers from more than 250 colleges across India, including IITs, NITs, and IIMs. In addition to the freshers, we are also planning to hire experienced candidates for various positions. By this time next year, we are planning to double our workforce.

Do you also feel that the right kind of talent is a challenge in the industry?

Up until a couple of years ago, everyone wanted to work for the so-called “Tech Giants,” but that is not the case anymore. Young graduates today prefer start-ups where they feel appreciated and challenged, somewhere they can explore and have the freedom to be creative. Although technically, we have grown past the bounds of a start-up, our ideals and practices remain the same. Another critical aspect of getting the right people on board is to be innovative. For example, Hexnode is an award-winning endpoint management solution offering some of the best features in the industry. Giving them an opportunity to be at the forefront of something like that is vital in hooking the right people. Attracting the right talent is difficult, but the real challenge is in retaining that talent. The current generation of the workforce tends to job hop frequently. Making them feel like they belong here and feel part of a family is essential for employee retention.

AI is projected to be the next market. How is AI contributing to the making of your products and services?

Exclusive Interview With Vinayak Shrivastav, Co

Video editing is the most crucial and at the same time most painstaking part of video making. Videos being the inevitable part of business promotion and content marketing strategies, there seems to be no escape from the frame-by-frame video editing process. Now AI has made this job easy for editors even allowing them to automate the trivial parts of the video editing process leaving them enough time and energy to think about the storyline. VideoVerse provides AI-enabled video editing technologies which include automated video editing, meta-tagging, smart live streaming solutions, and cloud-based editing services. Analytics Insight has engaged in an exclusive interview with Vinayak Shrivastav, Co-Founder & CEO, of VideoVerse.

1. Kindly brief us about the company, its specialization, and the services that your company offers.

Harnessing the power of artificial intelligence (AI) and machine learning (ML), Magnifi (by VideoVerse) delivers ground-breaking and innovative video analysis and editing technologies that are uniquely designed for users of all skill levels, offering platform solutions for unmatched transformation and seamless digital outreach of sports content. Using AI and ML technology to revolutionize how content is refined, analyzed, and consumed, Magnifi’s solution intelligently analyzes video footage based on specified, preset criteria. Content is analyzed in seconds, as opposed to minutes and hours, with clips being auto-resized and optimized to meet various social platform requirements so they can be auto-shared with speed and ease. Fully automated, and requiring no manual intervention, Magnifi offers both an API-based developer-friendly model and a full-service easy-to-use platform, to its customers. Magnifi’s solutions significantly reduce go-to-market time and costs while increasing ROI and opening new revenue streams for its enterprise customers.

2. With what mission and objectives, the company was set up? In short, tell us about your journey since the inception of the company?

Magnifi was founded by engineering pioneers and visionaries from the VC sector that were frustrated with the slow innovation and adoption of new-age technologies in the media sector. Sharing a passion for AI-led technologies and a desire to see the media sector evolve, the founders embarked on developing their platform technology to fill a major gap in the market, starting with the world of sports digital content and streaming.

Since its launch, Magnifi (by VideoVerse) has gone from strength to strength, securing contracts with major sporting groups and broadcast entities, while also securing $46.8M in Series B funding to help fuel its international growth. Today, the company operates from its headquarters in Mumbai, with office locations in New York, London, Israel, and Singapore.

3. Tell us how your company is contributing to the IoT/AI/Big Data Analytics industry of the nation and how the company is benefiting clients.

Magnifi’s technology is completely reimagining the “highlight reel” process in the sports industry, providing sports broadcasters, leagues, and clubs with simple editing tools that automatically capture key moments, from key plays and in-game scenarios to tracking specific players, fan interactions, and brand sponsor mentions.

OTT players, broadcasters, sports clubs and leagues, marketing agencies, e-gaming platforms, schools, colleges, and other businesses have found Magnifi’s product-market fit to be exceedingly strong.

4. Please brief us about the products/services/solutions you provide to your customers and how they get value out of it.

Magnifi is the flagship enterprise solution offered by VideoVerse. Magnifi endeavors to connect fans with real-time, relevant, and customized video content and make content discoverability easier. To this end, our proprietary AI models extract highlights and key moments from enterprise video content to auto-produce social-ready clips. The technology renders itself seamlessly to sports, news, and entertainment videos.

Exclusive Interview With Henrique Aveiro, Director Of Machine Learning/Ai, Insite Ai

Major disruptive technologies such as AI and machine learning are known for disrupting different sectors across the world with smart functionalities for human employees. These cutting-edge technologies created a massive impact, especially the post-COVID-19 pandemic in the retail industry. Companies have started leveraging AI and machine learning to revolutionize category management for improving the consumer-packed goods (CPG) managerial ecosystem. Here is an exclusive interview with Henrique Aveiro, Director of machine learning/AI, Insite AI where he explained to the readers how the company is focused on offering customized AI solutions to disrupt the CPG category management with strategic planning.

Kindly brief us about the company, its specialization and the services/solutions that your company offers, and how your customers get value out of it.

Insite AI is a provider of highly customizable AI solutions for CPG category management and revenue growth management. The company works with the Top 100 CPGs and consumer brands in the U.S. including Nestle Purina, The Boston Beer Company, and R.J. Reynolds, among others, to provide autonomous, completely explainable decision-making, and optimization for the most competitive assortment plans, trade promotions, and pricing possible. Powering this solution is a proprietary AI platform that provides transparent, predictive data and analytics with tangible, explainable recommendations with granularity down to an individual SKU on a shelf in a single store location. Insite AI is a customizable, end-to-end, cloud-based platform, therefore, it helps CPG brand leaders to make data-informed, strategic decisions that help maximize profitability and strengthen relationships with their critical retail partners.

Can you explain the historical pitfalls around AI/ML solutions in the CPG industry? Where have been the biggest gaps inhibiting success?

For years, the CPG industry and its category and assortment managers made decisions through a combination of planning software, manual analyses, and years of historical knowledge and gut instinct. The majority have continued to rely on these methods, either because they are skeptical of more sophisticated technology like AI and ML or are limited by overall buy-in from the C-Suite to adopt new methods that won’t always demonstrate immediate value.

What can AI/ML really do for CPG brands if implemented and used correctly? What are the biggest benefits?

For CPG brands, one of the most important elements to their business is strategic planning. Whether that’s in terms of shelf optimization, assortment, pricing, promotions— each change they make has a potential direct impact on end results. While things like planograms and older software may have at one point been helpful in guiding their decisions, the landscape has become increasingly challenging to navigate amidst ever-shifting consumer behaviors, disrupted supply chains, and shorter windows for decision-making than ever before. If used correctly, AI can not only funnel and analyze billions of data sets to show key industry trends, insights, and forecasting— it can actually make smart recommendations on what to do with that data once unlocked. It can therefore rid CPGs of guessing and provide an accurate view of opportunity or risk, generate predictive recommendations in real-time, gain intelligence on how products will sell at specific stores and the downstream effects of decisions like adding or removing products (SKUs), and understand how competitor product changes will affect overall demand. In order to be successful though, stakeholders should be asking three important questions to vet AI solutions upfront and ensure that they’re leveraging the best option for their brand: 1. Is the solution specifically designed for price and assortment optimization? 2. Is this going to help solve my particular business problems? After asking these questions, companies should also make sure that they’re choosing the right solution provider to partner with to help analyze and understand the data that they’re seeing. Without the proper collaboration, these solutions end up being more expensive, challenging and deliver poor results.

How does Insite AI address these challenges directly for your customers?

The key fact for the company is that it is not just about a software platform, the team combines deep CPG and AI subject matter expertise with a collaborative approach for customers. Insite AI provides a solution that is built within their own secure environment using their organizational, market, and customer DNA. The platform analyzes billions of data points to simulate possible primary and downstream effects from every proposed change to the product range, space trade-offs, pack configurations, changing prices, or offering trade promotions, among other options in long-range innovation and strategic planning.

How is AI/ML evolving today in the industry as a whole? What are the most important trends that you see emerging across the globe moving forward?

Exclusive Interview With Rajesh Dangi, Chief Digital Officer, Nxtgen Infinite Datacenter And Cloud Technologies

Know the depth of cloud computing with Rajesh Dangi, Chief Digital Officer

Tell us how your company is contributing in the cloud computing industry of the nation and how the company is benefiting the clients.

Cloud is a metaphor, it comprises multiple resources, services, and integration, thereof, and there are public, private, and hybrid clouds. Today everyone seems to be waking up to edge cloud and services thus the company has to be on the higher learning curve. With 5G, IPv6, satellite broadband the cloud services will get expanded to smart perimeters and localized computing infrastructure. NxtGenInfinite Datacenter and cloud technologies is teaming up with multiple private and government bodies to offer a range of services such as high-performance computing, distributed high-performance storage, smart edge services for BareMetal as a service, managed container services to traditional virtual instances, etc. with hardware accelerators, etc. to enable them with right products and services as heterogeneous cloud experience from core to edge. The emerging technologies such as AI/ML, AR/VR, data analytics, etc. are now a part of a standard offering that enable customers to leap forward on video conferencing, transcoding, smart surveillance, or video analytics, etc. for collaboration and employee wellbeing. The MLOps as a service helps them shorten the learning curve for the data management, feature engineering and model training as a single seamless workflow. These digital services are getting good traction along with digital transformations via consultative interventions to support them with quick and safe adoption.

What is your biggest USP that diﬀerentiates the company from competitors?

I will say it is not technology that differentiates the company but the way the industries use and integrate multiple technologies and provide fruitful outcomes sets the company apart. The team has solved the problems of customers working closely with them to help them augment newer technologies with unbeatable price and performance. The real differentiator is DNA for innovation, cost-effective and pays as one goes cloud and data center services with no cost consultative solutions and faster deployments just to mention a few, the company believes all customer requirements are unique and solving them together helps to win the deals and makes them choose this team as long term strategic partner than the box selling partner-driven selling done by most of the competition.

Which are the technologies you see customers will need more support in the near future?

Today’s cloud landscape for enterprises is distributed multi-cloud with edge enablement and none of the current CSPs have ability or reach to fulfilprim, private, public, and edge cloud requirements as single vendors. The mix of these technologies and resources are siloed and migration from one public cloud to another is becoming a nightmare, customers are fast choosing to cloud-neutral developments using open source with the ability to consume different services from diff vendors and still be able to effectively manage them. NxtGen is already ready with multiple cloud services including the latest hybrid (Unified core, private andedge cloud-ready), SpeedCloud application services platform that can cater multi-cloud, multi-tenancy, and multi-model deployment (BareMetal as a Service, HPC as a service, virtual instances, container services, high-performance storage, digital services for media engines, etc.) all via a single seamless dashboard with transparent resource metering for on-demand pay as you go model.

What’s do you think about trends / roadmaps of key technologies we can look for in the next few years?

Core computing will continue to become high-performance and multi-cloud hybrid environments will become defacto. The company has faster processors, storage and multi-giga speed networks. Edge computing will spread to rural India and help get more digital users and services bridging the digital divide. Smart edge locations and gateways with powerful hardware accelerators will transform the computing and storage landscape forever. Centralized seamless application deployment and up keeping from core to edge clouds will be the name of the game. Interconnects, private peering and internet services, and private networks will get more boosts from 5G, IPv6, and satellite broadband to ensure the reach, speed and capacity with smart routing for localized content delivery. AR/VR-enabled digital spaces will emerge as new channels for interaction and user experiences for learning, communicating, consuming and co-working areas. IoT and blockchains will see more adoption of mobile users, connected cars and things further expanding digital perimeters and ecosystems. Data science and analytics, MLOps, and open datasets will generate new career and product engineering opportunities since the growth of data has surpassed expectations and using the data for safer and meaningful outcomes is still a question of debate.Distributed high-performance data storage, data models with newer algorithms will bridge the gap between active and passive data stores and near real retrieval and insights with absolute redundancy for code, data and configurations by design. Data assets will get insured, traded and valued as a chúng tôi governance and privacy will remain a challenge as new technologies will continue to bring more vulnerable and remain susceptible to information security since traditional ways of data protection processes, tools and systems are still gaining the pace to tackle ransomware and insider threats. Industries are expecting tighter statutes, laws, and regulations and enforcement on data breaches.

What is your technology leadership mantra?

There is no silver bullet in technology management or rather a technology enablement to manage the outcomes from the tools and techniques available, in short— explore, experiment and enable. For large enterprises or even service providers, the culture of an organization plays a significant role and creates a positive impact on trying new things. The technology landscape is vast and dynamic as always, thus a“culture of creativity, conceptualization, and co-creation with customers” will become the ley tenets. Having a sponsor and awarding the efforts, celebrating failures and keep moving with newer technologies to solve current and anticipated problems is important.

Cloud computing is now one of the hottest cutting-edge technologies in the digital transformation era. Large private, public, hybrid, and multi clouds are available for users to utilize for data storage without direct active management. Each location of cloud computing becomes a data centre. The global cloud computing market is estimated to hit US$1,251.09 billion in 2028 with a CAGR of 19.1%. Here is an exclusive interview with Rajesh Dangi, Chief Digital Officer, NxtGen infinite Datacenter and cloud technologies, who elaborates the importance of cloud computing services and a wide range of high-performance and cost-effective cloud services for clients.Cloud is a metaphor, it comprises multiple resources, services, and integration, thereof, and there are public, private, and hybrid clouds. Today everyone seems to be waking up to edge cloud and services thus the company has to be on the higher learning curve. With 5G, IPv6, satellite broadband the cloud services will get expanded to smart perimeters and localized computing infrastructure. NxtGenInfinite Datacenter and cloud technologies is teaming up with multiple private and government bodies to offer a range of services such as high-performance computing, distributed high-performance storage, smart edge services for BareMetal as a service, managed container services to traditional virtual instances, etc. with hardware accelerators, etc. to enable them with right products and services as heterogeneous cloud experience from core to edge. The emerging technologies such as AI/ML, AR/VR, data analytics, etc. are now a part of a standard offering that enable customers to leap forward on video conferencing, transcoding, smart surveillance, or video analytics, etc. for collaboration and employee wellbeing. The MLOps as a service helps them shorten the learning curve for the data management, feature engineering and model training as a single seamless workflow. These digital services are getting good traction along with digital transformations via consultative interventions to support them with quick and safe adoption.I will say it is not technology that differentiates the company but the way the industries use and integrate multiple technologies and provide fruitful outcomes sets the company apart. The team has solved the problems of customers working closely with them to help them augment newer technologies with unbeatable price and performance. The real differentiator is DNA for innovation, cost-effective and pays as one goes cloud and data center services with no cost consultative solutions and faster deployments just to mention a few, the company believes all customer requirements are unique and solving them together helps to win the deals and makes them choose this team as long term strategic partner than the box selling partner-driven selling done by most of the competition.Today’s cloud landscape for enterprises is distributed multi-cloud with edge enablement and none of the current CSPs have ability or reach to fulfilprim, private, public, and edge cloud requirements as single vendors. The mix of these technologies and resources are siloed and migration from one public cloud to another is becoming a nightmare, customers are fast choosing to cloud-neutral developments using open source with the ability to consume different services from diff vendors and still be able to effectively manage them. NxtGen is already ready with multiple cloud services including the latest hybrid (Unified core, private andedge cloud-ready), SpeedCloud application services platform that can cater multi-cloud, multi-tenancy, and multi-model deployment (BareMetal as a Service, HPC as a service, virtual instances, container services, high-performance storage, digital services for media engines, etc.) all via a single seamless dashboard with transparent resource metering for on-demand pay as you go chúng tôi computing will continue to become high-performance and multi-cloud hybrid environments will become defacto. The company has faster processors, storage and multi-giga speed networks. Edge computing will spread to rural India and help get more digital users and services bridging the digital divide. Smart edge locations and gateways with powerful hardware accelerators will transform the computing and storage landscape forever. Centralized seamless application deployment and up keeping from core to edge clouds will be the name of the game. Interconnects, private peering and internet services, and private networks will get more boosts from 5G, IPv6, and satellite broadband to ensure the reach, speed and capacity with smart routing for localized content delivery. AR/VR-enabled digital spaces will emerge as new channels for interaction and user experiences for learning, communicating, consuming and co-working areas. IoT and blockchains will see more adoption of mobile users, connected cars and things further expanding digital perimeters and ecosystems. Data science and analytics, MLOps, and open datasets will generate new career and product engineering opportunities since the growth of data has surpassed expectations and using the data for safer and meaningful outcomes is still a question of debate.Distributed high-performance data storage, data models with newer algorithms will bridge the gap between active and passive data stores and near real retrieval and insights with absolute redundancy for code, data and configurations by design. Data assets will get insured, traded and valued as a chúng tôi governance and privacy will remain a challenge as new technologies will continue to bring more vulnerable and remain susceptible to information security since traditional ways of data protection processes, tools and systems are still gaining the pace to tackle ransomware and insider threats. Industries are expecting tighter statutes, laws, and regulations and enforcement on data breaches.There is no silver bullet in technology management or rather a technology enablement to manage the outcomes from the tools and techniques available, in short— explore, experiment and enable. For large enterprises or even service providers, the culture of an organization plays a significant role and creates a positive impact on trying new things. The technology landscape is vast and dynamic as always, thus a“culture of creativity, conceptualization, and co-creation with customers” will become the ley tenets. Having a sponsor and awarding the efforts, celebrating failures and keep moving with newer technologies to solve current and anticipated problems is important. Developing meaningful relationships within OEMs, industry partners, and start-up ecosystems with an open mind of co-creation to offer an end-to-end solution via multiple technologies is becoming an essential factor for digital transformation aspirants.

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