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 more 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 more 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 of 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 pattern of underlying cloud infrastructure is 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.
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