Machine learning (ML) is a subset of artificial intelligence (AI) and comprises a set of technologies that utilize a large number of data sets for training and testing. Although the concept of machine learning itself isn’t new - the term was first defined in 1959 - it has largely been out of the reach of organizational budgets.
Today, with machine learning services becoming an offering of many public clouds, it has become both affordable and accessible. Google Cloud Platform, Amazon Web Services, and Microsoft Azure, all offer ML with such ease of use that it no longer requires a team of data scientists to implement.
The coming together of machine learning and cloud computing has given rise to “the intelligent cloud” because ML has given birth to a number of new cloud services. A few of them are listed below:
Cognitive Computing enables apps to see, listen, talk, and make decisions with the use of ML technologies.
Cloud computing has greatly improved business intelligence - with intelligent insights and accurate forecasting - by merging BI platforms with ML-based tools.
Data-driven platforms on the cloud have made it possible for data to be captured from various sensors in large quantities, making IoT more intelligent.
Messaging platforms can now be integrated with bots who can respond to website visitor queries, and converse with them.
Voice-based personal assistants like Alexa, Siri, Google Assistant, and Cortana are all powered by machine learning to offer customized experiences for users.
Machine learning can now be easily leveraged, thanks to the cloud. With its pay-as-you-go model, it becomes even more easy to experiment with various ML capabilities and scale up or down any time. And thanks to machine learning, the cloud is now intelligent - learning from the vast ocean of data stored in it and creating better predictions and, thereby, smarter solutions.
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