{"id":8503,"date":"2023-12-26T08:00:48","date_gmt":"2023-12-26T16:00:48","guid":{"rendered":"https:\/\/live-cometml.pantheonsite.io\/?p=8503"},"modified":"2025-04-24T17:03:42","modified_gmt":"2025-04-24T17:03:42","slug":"containerization-of-machine-learning-applications","status":"publish","type":"post","link":"https:\/\/www.comet.com\/site\/blog\/containerization-of-machine-learning-applications\/","title":{"rendered":"Containerization of Machine Learning Applications"},"content":{"rendered":"\n
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This article will comprehensively create, deploy, and execute machine learning application containers using the Docker tool. It will further explain the various containerization terms and the importance of this technology to the machine learning workflow. The article will contain hands-on sessions with practical coding examples as a use case. Lastly, it will discuss the best practices when working with Docker and the future of containerization.<\/p>\n

What is Virtualization?<\/strong><\/h2>\n

To understand the concept of containerization, we need first to understand virtualization. Virtualization is the process of creating a non-physical entity that is made to appear as physical; it is a concept that dates back to the 1960s. A good example is a metaverse, where aspects of the physical world are mimicked in a simulated digital environment. In computing, virtualization creates a virtual copy of a physical IT infrastructure and resources such as hardware, application storage, network, desktop\/Operating system (OS), data, CPU\/GPU, and simulates functionality. It\u2019s more like distributing a physical machine\u02bcs’s capacity among many users or virtual environments. Virtualization has enabled cloud providers to serve their existing physical infrastructure to users. There are two forms of virtualization:<\/p>\n

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  1. Virtual Machines (VMs)<\/li>\n
  2. Containers<\/li>\n<\/ol>\n

    We need to ask what they virtualize to understand the difference between these two types of virtualization tools.<\/em><\/p>\n