Interaction - Tarun Dua, MD, E2E Networks

Tarun Dua, MD, E2E Networks talks about his organisation's work in Cloud computing.

How are the current AI-based cloud techs evolving and how do they make digitisation more effective?

Tarun Dua - Since 2022, adoption of AI and Machine Learning technologies have been growing at an exponential rate. As per McKinsey Global Survey, back in 2017, only 20 percent of organisations had implemented AI in at least one of their business domains. However, today, that proportion has skyrocketed to 50 percent. We saw the highest recorded adoption rate in 2019, reaching an impressive 58 percent. Every technology-enabled workflow, SaaS platforms, communication systems, healthcare technologies, customer support platforms, are poised to go through a disruption. The core of this disruption is driven by GPU-based machine learning and LLM technologies that work very differently to how we have built systems in the past.

We are now seeing interfaces with which we can communicate in natural language. This would dramatically simplify how users potentially interact with technology in the future. Natural Language Processing capabilities have advanced phenomenally, so has Computer Vision. Generative AI is now quickly able to generate product images, avatars and other creatives that businesses can use.

AI enabled Digitisation will rapidly accelerate because of the potential that these technologies open up for streamlining the moving pieces and simplifying the work we need to do for the digital migration.

What are the specific requirements of the hardware such as laptop configuration to support the latest generation of AI Computing Platforms ?

Tarun Dua - Most of the GPU-based machine learning algorithms are executed on Cloud GPU hyperscalers like ours, enabling nearly anyone with Internet access to start building and scaling AI/ML driven applications using NLP/Computer Vision and Generative AI techniques. The need for specialised hardware or laptops on the user’s end is minimal.

For instance, we recently launched a framework called Tir - which allows any data scientist or developer to train and build machine learning models on a Jupyter notebook environment virtually, eliminating the need for administrative efforts and specialized hardware.

AI First Hyperscalers  have democratised the access to high-end GPUs and ability to train and build machine learning models for anyone with a laptop of their choice.

What cloud technologies are most in demand from the large and small enterprises?

Tarun Dua - Companies increasingly prefer to migrate their entire development workflow; and build, launch and scale applications using the cloud instead of running physical colocated/on-prem environments. A recent study by Gartner suggests that by 2027, approximately 65% of application workloads will be well-suited or prepared for cloud delivery, marking a significant increase from the 45% recorded in 2022.

As a result, the most in-demand technologies are all related to running systems and frameworks that can run on the cloud, Kubernetes based container frameworks, cloud security, serverless architectures, DevOps, Data Science, and of course, machine learning technologies like NLP, Computer Vision and Generative AI. Rapid access to these Open Source based technology platforms are sought to be implemented very rapidly by all businesses that want to digitize and modernize their operations.

What are the challenges in your work and how do you resolve them?

Tarun Dua - Our company is an NSE-listed pioneering cloud infrastructure company based in India and several unicorns scaled on our infrastructure.

In the coming years, the core of our work at E2E Networks would focus on our vision of building a global AI-First Hyperscaler company based in India.

Our challenge at work is simple - to do whatever it takes to make this happen and provide the capabilities that modern data-driven businesses, Higher Education and Research and government bodies need in order to scale their AI/ML workloads and cloud computing workloads on our AI First Hyperscaler Cloud platform.

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