How an AI ready cloud is a necessity

Operating systems of the next era of tech

IBM Sep 12th 2018 A-A+

Transformation of the Cloud Platform

Cloud computing has become a mainstream element of modern software solutions as common as websites or databases during the past few years. The cloud computing market is dominated by few companies: IBM, Amazon, Microsoft, Google and AliCloud in China. The emergence of Artificial intelligence (AI) is proving to be disruptive enough to alter the existing dynamics of the incumbent cloud platforms as it is opening up opportunities for a new generation of cloud computing technologies.

AI in Cloud: The Next Generation Cloud Platform

With companies like IBM, Google, Amazon and Microsoft leading the charge in the last few years, AI capabilities have seen a tremendous level of investment in cloud platforms. As AI technologies are computing intensive, cloud platforms which provide computing resources on demand have become the preferred platform for AI. This server-centric paradigm is in sharp contrast to the previous era of client centered mobile and IOT where the demand for high end processors was not a highly critical requirement.
 
Most AI applications require very specific runtimes optimized for the GPU intensive requirements of AI solutions. For instance, a next generation cloud AI platform should be able to deploy a program authored using a deep learning framework like TensorFlow or Torch across hundreds of nodes that are provisioned on demand with optimal GPU capabilities.   While all the platform as a service (PaaS) providers have started incorporating AI capabilities, players who provide full stack support for AI are poised to gain the lead in this next era of cloud computing.

Architecture of the AI-first cloud 

The AI-first cloud is a next generation cloud computing model built around AI capabilities. The architecture of AI-first cloud platforms will have two major levels of functionalities.

a. Cloud Machine Learning (ML) Libraries that will enable the creation of machine learning models using a specific technology like TensorFlow Theano, Torch, Caffe, etc. These low-level AI tools will be used by start-ups and highly customized AI model developers.  Low Level AI support would also include on-demand availability of GPU optimized infrastructure.

b. AI Cloud Services: Technologies like IBM Watson with Natural Language APIs enable abstract complex AI or cognitive computing capabilities via simple API calls. This model will allow application developers to incorporate AI capabilities without having to invest in sophisticated AI infrastructures.

Watson on Cloud - The AI First Cloud:

Watson on the IBM Cloud allows companies to integrate the world's most powerful AI into applications which store, train and manage data in the most secure cloud. It not only provides high-end CPUs but also GPUs optimized infrastructure on demand. The key differentiation of Watson on Cloud from other Cloud Platforms is the availability of AI Cloud Services that most common application development would need such as unstructured data processing, real-time trend-detection, visual recognition, NLP, Tone Analyzer, Personal Insights, Chatbots and Data Refinery.

In short, Watson on Cloud is poised to become one of the leading operating systems of the next era of tech.