What Is Machine Learning - Machine Learning Has A Tremendous Offering To The Enterprise-But How?
Machine learning, a sub-component of artificial intelligence, is not new to the enterprise. But with techniques like deep learning, emulating human brain actions, increasingly gaining traction, businesses are identifying new and potentially transformative deployments of digitally disruptive technologies.
According to Algorithmia’s 2020 report, the main use cases for machine learning translate to customer service (i.e. chatbots) and internal cost reduction. But machine learning has applications far and wide. Dynamic pricing or surge pricing is essentially ML models that learn from corresponding factors that include customer interest, demand and history to adjust prices and entice purchases. Churn modelling is another application in telecom analytics where Machine Learning is deployed to predict which customers are likely to be lost and allowing corrective measures to be undertaken to mitigate the churn.
Currently, to Ensure Business Continuity in the Covid-19 era, more and more businesses are moving to the cloud, and the cloud is making AI and Machine Learning more accessible to the enterprise. Here are a few cloud deployments that find enterprise adaptability-
Machine learning algorithms are programs that can learn from data and improve from experience, without human intervention.
Machine learning (ML) is the study of computer algorithms that improve automatically through experience. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or infeasible to develop conventional algorithms to perform the needed tasks.
Machine learning is closely related to computational statistics, which focuses on making predictions using computers. The study of mathematical optimization delivers methods, theory, and application domains to the field of machine learning. Data mining is a related field of study, focusing on exploratory data analysis through unsupervised learning. In its application across business problems, machine learning is also referred to as predictive analytics.
AWS has also developed specific hardware for machine learning, with an inference chip known as Inferentia, which is intended for sophisticated applications such as search recommendations, dynamic pricing and automated customer support, and is accessible through the cloud.
Intended for enterprise use, Google Cloud’s AI Platform combines and integrates different aspects of the machine learning pipeline, from data storage and labelling to training to deployment.
Azure also has a focus on the potential perils of machine learning, building in so-called ‘responsible machine learning’ solutions to mitigate bias in models.
Summing up, with the proliferation of machine learning services on the cloud critically becoming indispensable to push down operational costs and opening up possibilities, expect enterprises to leverage the technology going forwards. ML will open up new methods of customer interaction, as chatbots are proving, and highlighting areas in need of efficiency, are enterprises ready for this massive change?
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