Computer Science That Has Evolved From Pattern Recognition...

‘Machine Learning is a sub discipline of Computer Science that has evolved from Pattern Recognition and Computational Learning Theory.’ ML is akin to Data Mining in the sense that both approaches look for patterns in the data set and while the former trains the program to better its understanding, the latter focuses on extraction of data for human comprehension. A typical application employing ML would involve the design and construction of an algorithm where the program is trained through huge samples of historical data to create a model. This model is later utilized on real time data sets to predict what happens next. While Machine Learning itself has been around for decades, it has found itself into reckoning with the advent of Big†¦show more content†¦Amazon Web Services S3 or Microsoft’s Azure are few amongst the various cloud services currently available that allow users to store massive amounts data at unbelievably low costs. With massive data at disposal comes the huge potential to analyze this data to draw inferences or predict future events. But why exactly is Machine Learning moving to the cloud? A compelling reason to move to the cloud would be the varying computational requirements in the ML lifecycle. The process of training/ retraining your model requires enormous computation power and resources, while the process of utilizing your trained models does not require any resources. The varying workload of the ML lifecycle makes it an ideal candidate for the cloud. Cloud Computing is a powerful technology that allows complex computations on massive scale data by eliminating the need to maintain dedicated storage space or maintain expensive hardware. Also it follows intuitively to train your models in the cloud where the data is stored. Prior to the advent of cloud based ML platforms, the only way to do advanced analytics was to purchase packages such as SAS or IBM’s SPSS or resort to tools like R. The cloud based platforms make Machine Learning more accessible to a large group of users who intend to deploy predictive models in order to enhance their products or services. Cloud based Machine Learning Services: