{"id":1436,"date":"2020-09-03T13:32:00","date_gmt":"2020-09-03T13:32:00","guid":{"rendered":"https:\/\/nag.com\/?post_type=insights&p=1111"},"modified":"2023-07-04T16:45:40","modified_gmt":"2023-07-04T16:45:40","slug":"a-low-cost-introduction-to-machine-learning-training-on-microsoft-azure","status":"publish","type":"insights","link":"https:\/\/nag.com\/insights\/a-low-cost-introduction-to-machine-learning-training-on-microsoft-azure\/","title":{"rendered":"A Low-Cost Introduction to Machine Learning Training on Microsoft Azure"},"content":{"rendered":"
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Machine learning is becoming ever more powerful and prevalent in the modern world, and is being used in all kinds of places from cutting-edge science to computer games, and self-driving cars to food production. However, it is a computationally-intensive process – particularly for the initial training stage of the model, and almost universally requires expensive GPU hardware to complete the training in a reasonable length of time. Because of this high hardware cost, and the increasing availability of cloud computing many ML users, both new and experienced are migrating their workflows to the cloud in order to reduce costs and access the latest and most powerful hardware.<\/p>\n

This tutorial demonstrates porting an existing machine learning model to a virtual machine on the Microsoft Azure cloud platform. We will train a small movie recommendation model using a single GPU to give personalised recommendations. The total cost of performing this training should be no more than $5 using any of the single GPU instances currently available on Azure.<\/p>\n

This is not the only way to perform ML training on Azure, for example, Microsoft also offer the\u00a0Azure ML product<\/a>, which is designed to allow rapid deployment of commonly used ML applications. However, the approach we will use here is the most flexible as it gives the user complete control over all aspects of the software environment, and is likely to be the fastest method of porting an existing ML workflow to Azure.<\/p>\n

Requirements<\/h2>\n

To follow this tutorial you will need:<\/p>\n