The cloud as we know it is going through a massive transformation. What started as an on-demand subscription service for consuming compute, storage and the network is no longer the same. The compute component of the cloud has become table stakes for the vendors in the infrastructure business. Spinning up virtual machines, adding storage and configuring a network is no longer exciting for veteran users. The cloud in its latest avatar is emerging as a data-centric, intelligent platform ready to deal with the next generation of applications and workloads.
At the front and center of this emerging intelligent cloud is Machine Learning (ML), which is undoubtedly the most disruptive technology of this decade. The rise of powerful computing environments based on GPUs and FPGA, combined with cheaper but equally powerful storage backed by SSDs, made it possible to store and process massive datasets. Machine Learning only gets better with additional data. Over a period, ML programs become intelligent enough to make smart decisions by themselves without the need for human intervention. The datasets required by the ML algorithms are generated by both cloud providers and the customers running their workloads in the cloud. The logs and usage patterns of underlying cloud infrastructure are helping the vendors drive better utilization of servers. As customers consume managed services offered by the cloud platform, they generate additional data, which becomes a gold mine for cloud providers. The bottom line is that the cloud has all essential components – ample compute power, abundant storage backend, massive amount of data – to deliver compelling Machine Learning capabilities. This new ability is enabling cloud providers to offer scalable Machine Learning platform in the cloud.
If the first (PaaS) and second generation (IaaS) of cloud were based on compute, the next generation cloud is built on the solid foundation of Machine Learning. Cloud providers are offering next generation of services that are based on ML. When developers and customers use these services, they indirectly improve the underlying ML engine through new datasets.
In the recent past, top cloud providers including Amazon, Google, IBM, Microsoft and Salesforce started to emphasize cognitive computing and intelligent cloud. Microsoft was one of the first to talk about this trend by branding Azure as an intelligent cloud. Google, which has top dollar set aside for research and development, is positioning its cloud as the next generation data-centric platform. Amazon has launched a new set of services under the Amazon AI branding. IBM, through its Watson investment, is pushing the agenda of cognitive computing. Salesforce and IBM recently announced a partnership to collaborate on ML.
The next generation cloud powered by Machine Learning will offer services for building applications based on cognitive computing, predictive analytics, intelligent Internet of Things, interactive personal assistants and bots. The APIs exposed by these services will democratize Machine Learning and Artificial Intelligence by empowering developers. By consuming a simple set of APIs, developers will be able to build highly sophisticated applications driven by intuitive user experiences.
Machine Learning is slowly but steadily taking over the cloud. The adoption of this brand-new cloud will not only result in better revenues for the providers but also helps them deliver better capabilities that are driven by data. At the risk of sounding clichéd, I want to conclude by saying: data is the new oil of the digital economy.