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India Should Focus On Building Data Infrastructure First, Before Charging Into AI Race

India Should Focus On Building Data Infrastructure First, Before Charging Into AI Race

Artificial intelligence is permeating into the Indian tech atmosphere. For this to happen at a faster pace, it needs considerable investment on resources and equipments. While AI is making headway in India with the support from the government, creating an AI-friendly ecosystem is a formidable task. It requires not only the right infrastructure, but needs also needs proper data infrastructure to ensure appropriate storage and networking for seamless functioning.

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This article discusses the essence of building a data infrastructure in AI to grow substantially by articulating key features which give an advantage in achieving the popular tech phenomena.

Building Right Infrastructure For AI

Since India is witnessing a significant rise in the number of companies in the domain of AI and ML, it is imperative that there should be enough focus on handling and storing the ever-growing data. It goes without saying that data is at the core of AI and companies should focus on creating the right storage framework. For example, complex neural networks, which are generally data intensive, need to have a vast and flexible storage infrastructure in line with their computing resources.

Another aspect of data to be looked into is its source. AI companies need to identify what data goes into their products and applications. This largely affects storage requirements for AI. Once data storage requirements are finalised, the plan to bring in a data infrastructure is idealised by pooling the right resources for the project. Since the Indian tech market is witnessing an influx of investment in development, AI applications can leverage these opportunities to fulfill data infrastructure.

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AI And IoT Are Brothers-In-Arms

Research firm Gartner predicts that by the end of year 2020, more than 20 billion devices will be connected by internet of things. Surge in IoT devices would generate enormous amount of real-time data, which will have to be analysed and processed accurately. This is possible only through technologies such as big data and AI.

IoT has expanded its reach into digital avenues substantially. Peter Middle, research director at Gartner says, “Apart from automotive systems, the applications which will be most in use by consumers will be smart TVs and digital set-top boxes. Smart electric meters and commercial security cameras will be most used by businesses.” The reason AI comes into IoT’s purview is because it offers the capability of providing a powerful computing environment along with making IoT better with insights.

Improving Computing Power To Deal With Large Data Sets

The good old days of CPU-based computing are no longer applicable for processing in the powerful applications such as AI and ML. CPUs only extend capabilities for basic AI applications. Cheryl Adams, an IT expert, brings out the intrinsic challenge in building a powerful AI. She says, “AI applications and the loads that they can put on a system’s resources will vary in volume and complexity. To support deep learning, problem-solving, reasoning, and learning, these applications need the ability to analyse large volumes of data in various formats, then compile data and produce results in an optimal amount of time”.

Companies implementing AI need to shift to GPUs instead of CPUs since they provide significant computing power. Famous graphics-chips manufacturing companies such as Nvidia are already building the next-gen processors dedicated to AI. If Indian companies plan to focus on building an effective AI ecosystem, it is necessary they leverage processors such as GPUs rather than implementing conventional CPUs.

Right Data Infrastructure + Right Mindset

Another crucial factor to AI’s success is the organisational mindset towards its implementation. Not everyone is likely to agree with developments in this space. This should be carefully scrutinised before the AI is deployed on a large scale. Companies should also analyse how their employees fare in the AI and ML skills section. They should invest not just on AI infrastructure but also on the right people such as data analysts, data engineers and researchers. They hold the key to the company’s AI success.

AI should also cater to social well-being of the country. With not just delivering solutions, but also the betterment of human lives. A survey by Pricewatercoopers India highlights that 60 percent of the participant companies opine that AI is here to tackle social issues along with making their business future-ready.

Conclusion

Building an exclusive AI data infrastructure in the Indian ecosystem will be quite challenging. However, if companies concentrate and improve on the above mentioned factors, which have a considerable impact on AI, they are likely to be successful. Also, it is important that these businesses have AI as one of their core functionality which makes easier to establish a data infrastructure.

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