- Published on June 29, 2022
- In AI Features
Council Post: Verticalisation of AI platforms – how to move from horizontal
A vertical AI strategy provides the opportunity to enterprises to have a laser-focus on a particular domain or a use case, adapting well to the customer's need as compared to horizontal platforms.
- By Vinay Kumar Sankarapu
There has been a surge in demand for AI, clearly visible in enterprises and small firms alike. To keep up with the ever-changing technological landscape, regulatory and legal environment and customer demands, enterprises are devising new ways to scale their AI projects, especially in mission-critical functions.
Yet, before enterprises formulate ideas or experiment with AI, they need to map the use case they are looking to solve and measure the solution against this use case. There are multiple ways to deploy AI, and if a particular strategy provides efficient implementation and results, only then should it be implemented.
Interestingly, the adoption of AI has primarily been driven by horizontal or general-purpose platforms so far. A newer strategy, ‘Vertical AI’ is no
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Vinay Kumar Sankarapu
Youngest member in 'AI' task force setup by Commerce and Industry Ministry of India to propose and recommend the path/policies for 'AI' adoption in India, Forbes Asia 30 under 30 member in technology; Public speaker in industry and Tech conferences like GTC (Nvidia), TEDx, Re-work, Nasscom etc; Bachelors and masters from IIT Mumbai, Published Author of two novels, Received an excellence award for my research in my third year of college, Research on particle formation and predictive modeling in Laser Ablation.
Founded arya.ai, a Deep Learning startup in my fourth of college. Since then, I have been heading research and product in Arya.ai with key focus in building advanced Technology for enterprises.
I believe, technology should be easy to use and be an enabler for building a great product. The deliverables and capability of the product depends on depth of the technology advancements. 'Deep learning' is one such technology that has huge potential and can solve many of the todays complex problems. We wanted to take the research level advancements of Deep Learning to the hands of every developers and enterprise by simplifying and automating the complex steps such that they can leverage it and build next generations products.
Our key focus is in building tools and systems to simplify the complex tasks of building 'Deep Learning' systems.
Interests: Deep learning, AI, Particle Physics.
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