Artificial intelligence: The growth factor for Cloud GPU market

Artificial intelligence increasingly becoming a part of various software systems and products across different verticals like retail, healthcare and so on

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According to a report by IDC, worldwide spending on artificial intelligence systems is forecast to reach $35.8 billion in 2019, an increase of 44.0% over the amount spent in 2018. The report also predicts that the retail sector will lead the spending, followed by the banking sector. Artificial intelligence is well-positioned to impact various sectors like retail, healthcare, banking, finance, discrete manufacturing, transportation, etc. According to a Gartner survey, 37% of organizations have implemented AI in some way.


In the early stages, AI was based on rule-based systems, in which, the AI system depended on a knowledge base of rules to deliver business value. These systems were limited by how well the rules were defined by human experts. Machine learning - a subset of AI - is entirely different; once a machine learning model is trained, the model can learn on its own through the newly available datasets, without depending on human expertise heavily. The latest advances in AI are majorly accomplished using machine learning and its subset deep learning, which uses artificial neural networks.

Running machine learning workloads often requires vast amounts of compute power, which dramatically increases the cost of machine learning when traditional CPUs are used. Instead, using GPUs can decrease the cost of deep learning model development and at the same time increases throughput with massively parallel AI workloads. A GPU contains several hundreds of cores that can process simple computations in parallel, which is often necessary for machine learning and deep learning workloads.

The most advanced datacenter GPU, NVIDIA Tesla V100, can deliver dramatic performance for AI and machine learning workloads, reducing the time required and the total cost of training machine learning models. When it comes to the Indian Cloud GPU market, the available options are very limited. But the Indian startup ecosystem has been vibrant with various healthcare, retail and many other companies working with AI and machine learning systems to deploy them into newer use cases for processing natural language text, voice generation and/or recognition in multiple languages, image recognition and object recognition in videos. This growing demand has been the major reason for building the GPU Cloud.


In Conclusion

Whether it is retail, healthcare, banking, or entertainment, AI is growingly becoming a part of various software systems and products. Organizations should, if they haven’t yet, start developing AI capabilities and should work on how they can solve various business problems and create value with AI-based approaches. In this quest, AI is also becoming the differentiating factor for sustaining competitive advantage and creating superior customer experiences.

By Tarun Dua, managing director and co-founder, E2E Networks