Kesava Reddy, Chief Revenue Officer, E2E Networks discusses AI and Cloud computing here.
How is AI shaping Cloud Computing technology in 2024?
Kesava Reddy - Recent reports predict that the global AI market is projected to reach $407 billion by 2027, with a 37.3% annual growth rate between 2023 and 2030. In the Indian market, IndiaAI initiative by MeitY expects it to contribute $500 billion to India's GDP by 2025, accounting for 10% of the country's target of $5 trillion GDP.
The increasing adoption of AI signifies a swiftly expanding demand for cloud platforms, infrastructure, and services specifically tailored for AI. Technologies such as Generative AI and other emerging AI tools are founded on deep learning models and necessitate AI-focused infrastructure, such as InfiniBand-supported HGX H100 clusters or advanced cloud GPUs like the A100. Additionally, a variety of other cloud technologies are essential for accelerating the training and inference stages of AI models. Consequently, as Generative AI becomes integrated into applications and services at scale across various sectors and industries, cloud computing models are evolving to meet the requirements of this technology.
Furthermore, in the year 2024, we expect to see emergence of agent-based AI models, which will significantly impact various sectors such as productivity, education, shopping, and entertainment. This would drive further transformation of cloud computing technologies, as these systems require high-end cloud GPUs and AI-first cloud service providers to reduce latency that real-time responsiveness requires.
Do you use a partner network to provide AI-enabled Cloud Computing? What is the strength and expanse of your partner network?
Kesava Reddy - Yes, we have a partner network to provide AI-enabled cloud computing services and AI infrastructure to organizations, developers, researchers and innovators. This network is a critical component of our strategy and has been seeing rapid growth, primarily because E2E Networks is recognized as a leading provider of advanced AI infrastructure in India.
Our partner network operates through two distinct models - the Sell To and Sell Through models. These models are designed to accommodate different partnership preferences. We also offer the highest margin in the cloud industry to our partners in both these models. In the Sell To model, partners act as connectors between E2E Networks and the end customers. In the Sell Through model, our partners actively resell E2E Networks' cloud services. This model empowers partners to offer our state-of-the-art AI infrastructure directly to their clients.
The reason why we have seen massive incoming interest from partners is due to our unique position in the market. We are amongst the only providers of advanced cloud GPUs and AI-first infrastructure in India, catering specifically to AI companies' needs. Our focus on the niche has garnered significant interest from potential partners, who are seeing a rapid growth in AI adoption.
What are the challenges in AI-enabled Cloud computing and what are the ways to resolve them?
Kesava Reddy - AI-enabled cloud computing promises powerful innovations, but this exciting intersection also poses unique challenges.
Global Challenges -
- Technical Complexity: Integrating AI with existing cloud infrastructure can be intricate, requiring specialized skills and potentially disrupting workflows. Open-source tools and standardized interfaces can ease integration.
- Data Privacy and Security: Sensitive data processed in the cloud raises concerns. Strong encryption, transparent data governance policies, and user control over data usage can bolster trust.
- Resource Intensiveness: AI algorithms, especially deep learning, can be resource-hungry, increasing costs and environmental impact. Optimizing algorithms and using resource-efficient hardware are crucial.
Challenges Specific to India -
- Digital Divide: Unequal access to internet infrastructure and technological literacy hinders cloud adoption, especially in rural areas. Bridging the digital divide is crucial.
- Limited Data Availability: Lack of high-quality, labeled data for training AI models poses a challenge.
Resolutions for the Indian context -
- Promoting localized AI solutions: Develop AI applications relevant to India's specific needs, like agriculture, healthcare, and education.
- Building a robust AI ecosystem: Encourage collaboration between academia, industry, and government to foster innovation and knowledge sharing.
By addressing these challenges and harnessing the potential of AI-enabled cloud computing, India can unlock significant economic and social benefits, leading to a more inclusive and technologically advanced future.