Cloud service providers (CSPs) of the future to offer more “intelligence”


Cloud service providers (CSPs) are beginning to offer intelligent technologies to enterprise customers as a way to separate themselves from competitors, according to a recent market report.

“To grow market share, many cloud service providers (CSPs) are introducing specialized compute instances, which target data-intensive workloads and ease the integration of artificial intelligence (AI) and machine learning (ML) into enterprise business applications,” concludes IHS Markit’s Cloud Services for IT Infrastructure and Applications Market Tracker. 

As a result, IHS Markit says, the off-premises cloud service market is expected to reach $374 billion in 2022, nearly three times the roughly $130 billion in 2016. In particular, the report says, AI and ML will find uses in the high-growth cloud-as-a-service (CaaS) and platform-as-a-service (PaaS) segments.

“Innovative service offerings by CSPs are multiplying, including the introduction of blockchain technology in PaaS service offers,” IHS Markit says.

It’s not just the scrappy upstarts, either: Cloud heavyweights have been rolling out smart services.

“Amazon made a smart move when it integrated Alexa into Amazon Web Services business applications — and by launching several machine learning services, further expanding its breadth of intelligent solutions,” says Clifford Grossner, IHS Markit’s senior research director and advisor for the company’s cloud and data center research practice. “Google and Cisco also upped their AI and ML game, targeting hybrid cloud deployments with a collaboration aimed at running these tasks, both on-premises and from Google Cloud.”

Microsoft, meanwhile, is making moves to help AI developers build apps to run on its Azure cloud. Redmond’s Project Brainwave, announced in May at the company’s Build Developer conference, allows developers to use field-programmable gate arrays (FPGAs), which enable AI models to be processed faster and can be reconfigured even after being installed in an Azure server.

And IBM is applying AI to storage management through Storage Insights, which collects usage and performance data from a range of IBM storage customers that is uploaded to IBM’s cloud, where it is analyzed using machine learning algorithms to determine optimal settings for specific workloads on specific equipment. The ML algorithms can provide setting recommendations based on the performance goals and pricing goals of the customer.

For years the cloud has offered enterprises flexible storage, improved performance, and lower costs. Today those benefits are table stakes. The successful cloud of the future must provide customers with intelligence and unique insights. Wisdom, even. The intelligent cloud beckons.


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