Everybody is talking about machine learning. Because it promises vast benefits that could impact every aspect of human life. Efforts have been made to develop machine learning to a stage where there will be no human intervention necessary. This section of Artificial Intelligence, which uses machine learning models to learn from data, is the next level of evolution in automation. When coupled with the power of cloud computing, machine learning could be even more beneficial. This fusion is known as ‘the intelligent cloud.’

The current usage of the cloud includes computing, storage and networking. Since the feature of machine learning is infused in the cloud, the capabilities of the cloud will increase vastly. The intelligent cloud becomes capable of learning from the vast amount of data stored in the cloud, to build up predictions and analyze situations. This will serve as an intelligent platform to perform tasks efficiently. Cloud computing provides two basic prerequisites for efficiently and cost-effectively running an AI system. First, it provides scalable, low-cost computing, and secondly, it is a great way to store and process large volumes of data. Therefore, the fusion of the cloud with machine learning benefits both these disciplines. The impact of machine learning on the cloud is greatest in the following aspects:

Cognitive computing:

Machine learning in the cloud does exactly cognitive computing. The large amounts of data stored in the cloud provide a source of information for the machine learning process. With millions of people using the cloud for computing and storage, the already existing data, the millions of processes that happen every day, all provide a source of information for the machine to learn from. The whole process will provide applications in the cloud with sensory capabilities. The applications will be able to perform cognitive functions and make decisions. Cognitive computing systems are more at an experimental stage today and doing minimal importance tasks. Over time, we can expect these systems to take over healthcare and hospitality, business and personal lives even.

Personal assistance and healthcare systems:

Personal assistants have made life easier for individuals. The products such as Apple Siri or Microsoft Cortana are pre-coded voice recognition systems that give a feel of human touch to machines. But these personal digital assistants have limited capabilities. The responses are pre-programmed into the system, and most of the queries are more general than personal. With the mass data on the cloud, the learning capabilities of machine learning, and its cognitive computing feature as mentioned above, personal assistance can almost replace any form of human interaction.

Such capabilities can be great assistance in running huge businesses. Imagine a computer that can access all the information on your past transactions, analyze the present sales and make predictions on future profits! Plus, it also tells you where the functioning is at fault and what can be done to correct it.

The need for an intelligent cloud in fields like healthcare is also very appealing. It would not act as a replacement for doctors or their procedures. Rather, it can act as a virtual assistant to decide the right methods to be used in the treatment of the patients. The machine can gather years of information on a particular case, make comparisons and recommend new approaches to treatment to make the process easier on the doctors.

Conclusion:

With great progress happening in the development of both machine learning and the cloud, their future seems increasingly tied together. Cloud computing becomes much easier to handle, scale and protect with machine learning. Not just that, the wider the business initiatives get on the cloud, the more the cloud will need machine learning to be integrated, to make it more efficient. There will be a point in time when no cloud will exist without machine learning.