Subsequently, we’re going to install a Jupyter Notebook with Docker.
#How to install jupyter notebook on docker how to
We’ll therefore also cover what Docker is, briefly how it works and how to install it to your system. Fortunately, with Docker, we can remove many of those problems by abstracting away the host machine. due to different software installed on the machine, or different operating systems that are in play).
This could have been problematic, as everyone’s host machine works differently (e.g. Subsequently, we are interested in actually installing such a Notebook onto your system. What can it be used for? How can it help? This is what we will try to answer. Firstly, we’ll take a look at what a Jupyter Notebook is. It’s widely used in the data science community and therefore deserves a more prominent role on MachineCurve and in any future article I write. Then came Python environments, then came Anaconda, but today we will cover Jupyter Notebook. In the past, this meant that everyone had to install dependencies on their own systems. In my experience, success factors of data science and machine learning projects – or any software project in general – include that runtime environments are shared. If you’re familiar with deployment tools, you can even deploy the model in the field, for example by means of a web service. I’d argue that very similar things are true for those who research and engineer machine learning models, as breakthroughs in the areas can directly be captured. Being a data scientist could mean that you have the sexiest job of the 21st Century, according to some business literature.
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