BRINGING AI AND MACHINE LEARNING ACCESSIBLE TO ENTERPRISES CREDIT TO CLOUD
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-
Amazon’s cloud service, AWS offers a wide range of machine learning solutions on the cloud, with Amazon claiming that more machine learning happens on its platform than anywhere else. Of particular note is Amazon SageMaker, which is focused on simplifying the process of building, training and deploying machine learning models. It does this in part through a web-based visual interface allowing for the uploading of data, the tuning of models and comparisons of performance.
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.
Google is perhaps the company most associated with machine learning, thanks to its development of the open-source TensorFlow platform, as well as its association with one of the most advanced machine learning companies – DeepMind and its programs such as AlphaGo.
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.
Microsoft’s Azure cloud platform has built-in machine learning services for enterprises looking to bring machine learning models to bear. With a stated focus on MLOps, the subset of DevOps dealing with correct machine learning development practices, it includes both code-based and drag-and-drop environments to accommodate users of all skill levels.
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?