Rise of AI-as-a-service – Cloud services driving AI adoption

Blog: NASSCOM Official Blog

Bringing AI to the masses

The benefits of AI initially accrued to early adopters with strong IT infrastructures and spending capacity. These early adopters have now rolled out cloud-based AI services, bringing AI to the masses. A robust “as-a-service” marketplace is emerging around AI that will give enterprises more options and flexibility for accessing AI capabilities [1]. AI-as-a-service (AIaaS) or cloud-based AI incorporates an array services that provide AI tools through cloud computing services. APIs and services from public cloud providers can be consumed without the need to create custom machine learning models. These services leverage the underlying infrastructure owned by the cloud vendors and provide the analytical environment without having to invest heavily.

Cloud computing has subsequently moved from being seen as a cost-effective way of data storage to becoming an integral part of advancing AI and other cognitive capabilities in the enterprise [2]. A survey by Deloitte highlighted that 49% of companies that have deployed AI today are using cloud-based services [3]. Having large public cloud infrastructure and AI platforms, Microsoft, Google, Amazon, and IBM have stepped up as major players in the AI “as-a-service” race.

Cloud-based AI and its benefits

Setting up the right environment could be complex, cloud providers are therefore offering pre-configured Virtual Machine (VM) templates coupled with popular frameworks like TensorFlow, Microsoft Cognitive Toolkit (CNTK), Apache MXNet, Caffe and Torch [4]. These VMs are backed by GPUs training complex neural networks and machine learning models. AIaaS provides several advantages over traditional mechanism including advanced infrastructure at minimal cost, transparency in business operations, and scalability. For AI to be optimized, it is also essential to have an entire ecosystem of partners [5].

AWS partners with NVIDIA to offer its GPUs in the cloud
Siemens Mindsphere offering by baking SAS analytics into its SaaS based Industrial IoT software platform

A recent Deloitte report highlighted that among companies adopting AI technology, 70% will obtain AI capabilities through cloud-based enterprise software, and 65% will create AI applications using cloud-based development services [6].

Road ahead

AIaaS will help organizations overcome barriers to adoption and is expected to drive full-scale AI implementations, better ROI and will subsequently result in higher AI spending. Public cloud providers are investing heavily in AI to attract customers to their platforms. AI in the public cloud is still at nascent stages and there are massive opportunities for the AIaaS market to proliferate. Cloud is turning out to be an essential driver for AI, specifically for the adoption of compute and data services.

References

[1] https://advisory.kpmg.us/content/dam/advisory/en/pdfs/2019/8-ai-trends-transforming-the-enterprise.pdf

[2] https://www.information-age.com/cloud-and-ai-adoption-123485106/

[3] https://www2.deloitte.com/us/en/insights/focus/signals-for-strategists/artificial-intelligence-and-devops-for-cloud-computing.html

[4] https://www.forbes.com/sites/janakirammsv/2018/02/22/the-rise-of-artificial-intelligence-as-a-service-in-the-public-cloud/#40a8f2ed198e

[5] https://www.forbes.com/sites/danielnewman/2020/01/07/why-ai-as-a-service-will-take-off-in-2020/#15be75703366

[6] https://www2.deloitte.com/us/en/insights/industry/technology/technology-media-and-telecom-predictions/cloud-based-artificial-intelligence.html

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