From the course: Docker for Data Scientists
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Why Docker?
- [Instructor] Now one of the skills and requirements of machine learning, or data science, is code reproducibility. So you've created some amazing machine learning model, and then others need to be able to verify it. So it's a bit like a research project. If you've ever tried using Python's virtual environments, you know that this can be pretty messy, and Docker's one of the answers to this problem. Docker's becoming so popular that you'll find official images for various Linux distributions, all the way to deep learning frameworks. There's also a thriving community, and so you can often find images of software that you are looking to deploy on Docker Hub. Now one of the things you're probably thinking is if Docker is so amazing, are Virtual Machines going away any time soon? Now, Virtual Machines, or VM's, are heavily embedded in enterprise IT today, and nothing will be replacing them any time soon. So let's compare Virtual Machines and Docker, and let's look at Virtual Machines…
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