Leveraging AI clouds to transform industries into powerhouses

Integrating Artificial Intelligence (AI) and Machine Learning (ML) with cloud computing is transforming business operations globally, and India is at the forefront of this change. Today, enterprises in India are adopting AI-powered automation and intelligent systems to streamline operations, enhance user experiences, and gain a competitive edge.

Reports indicate that the adoption of Cloud computing is set to grow at a compound annual growth rate of 24%, with the public cloud market projected to reach approximately USD 13 billion by 2025. About 85% of businesses in India are currently utilising external cloud providers for analytics, and the country is well positioned as a leader in adopting these technologies, outpacing nations like Germany and Spain.

Shaping the Industries

The synergy between AI, ML, and cloud computing drives innovation today. Why? Because cloud platforms provide the necessary infrastructure to deploy foundational AI models efficiently. This allows enterprises to build AI-powered workflows using architectures like Retrieval-Augmented Generation that use cloud-hosted AI models in conjunction with Vector Databases or Knowledge Graphs. Such workflows can simplify operations, enhance customer experience, and improve search or recommendation in their products. This helps shape industries like e-commerce, retail and customer support.

By deploying open-source foundational AI models on their infrastructure, businesses can retain control over their data and ensure compliance.

One of the biggest benefits of the convergence of AI/ML and cloud computing is the ability to build data-sovereign AI. By deploying open-source foundational AI models on their own infrastructure, businesses can retain control over their data and ensure compliance with laws and regulations. With a data-sovereign AI stack, industries such as fintech, healthcare, and banking can reap the benefits of emerging AI workflows. One example of where this can have a massive impact in the future is anomaly and fraud detection.

The other sector that is going to see widespread transformation is healthcare. AI-powered diagnostic tools can assist doctors in analysing patient data more accurately and swiftly. For example, healthcare startups can use cloud-based AI models to process medical images, helping radiologists in resource-limited areas to deliver faster diagnoses.

Beyond using Artificial Intelligence models directly in cloud infrastructure, the emergence of AI as a Service (AIaaS) is democratising AI deployment.

The world is also seeing quick adoption of these technologies in manufacturing, where vision AI is helping improve safety and bring predictive maintenance to factory floors. Predictive maintenance systems powered by vision or multimodal AI can monitor machinery health, reducing downtime and improving productivity. Parallelly, e-commerce companies are leveraging AI to manage their product catalogues, personalising user experiences in real-time.

With India’s e-commerce market forecasted to reach USD 200 billion by 2026, AI and ML models for customer behaviour analysis, recommendation engines, and inventory management are becoming essential. NASSCOM data indicates that nearly 45% of large Indian enterprises have incorporated AI into their core processes, a very high number compared to developing or developed countries globally.

Beyond using AI models directly in cloud infrastructure, the emergence of AI as a Service (AIaaS) further democratises AI deployment.

Democratising AI and ML

AIaaS simplifies how businesses access advanced AI, making it feasible for small and medium-sized enterprises to implement AI solutions without extensive technical expertise. This democratisation is particularly significant in our country, where over half (65%) of businesses utilising AI report that productivity gains have offset initial setup and deployment costs. Startups increasingly adopt AIaaS models to enhance their products, demonstrating the future’s promise.

By combining vision AI with data processing, businesses can analyse large datasets rapidly, allowing for quicker problem identification and solution.

Very soon, we will see the widespread adoption of AI cloud, that is, systems where AI workflows are prebuilt with self-learning systems capable of understanding and reacting to data. These systems will be vital in predictive analytics. The other technology that will play a pivotal role will be integrating AI cloud with mobile apps. This will allow low-power mobile devices to offload intensive computational tasks to large AI models hosted on the cloud, enhancing their real-time generation and prediction capabilities.

Intelligent Systems in the Cloud

The recent launch of the NVIDIA H200 Tensor Core GPUs on the Cloud is poised to enhance AI’s capabilities and ML-powered intelligent systems significantly. More GPU computational power means more sophisticated machine-learning models and a lower total cost of ownership. This is essential for scaling training and inference of massive-scale AI models, such as the Llama 3.2 Vision or Llama 3.1 405B models, which are memory-intensive and cannot be scaled on low-end GPUs. The advanced GPU parallelism allows for real-time AI inference, from which numerous products can benefit.

Sectors like logistics are now witnessing a mix of AI models being deployed. By combining vision AI with data processing powered by technologies like RAPIDS, businesses can analyse large datasets rapidly, allowing for quicker problem identification and solution implementation. On the other end of the spectrum, we have AI-powered systems for AgriTech and Smart Cities, which can substantially improve efficiency and workflows. In contrast, cloud GPUs like the H200 can provide the much-needed computational power for these large-scale applications.

As we look to the future, the rapid advancements in AI and ML, combined with the power of cloud computing, will redefine how businesses operate across all sectors. The ongoing development of AI frameworks and AI agents, along with improvements in cloud infrastructure, will allow even smaller enterprises to build intelligent, scalable systems. AI-driven automation will expand from routine tasks to more complex decision-making processes, enabling businesses to make real-time, data-driven decisions.

Additionally, as cloud technologies evolve and powerful GPUs like the H200 become accessible, the ability to seamlessly integrate AI models into business processes will become a standard, pushing the boundaries of what is possible in automation and intelligent systems worldwide.

By Mohamed Imran

The author is the CTO of E2E Networks.

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