In the Conda Essentials course you learned how use the Conda package manager to create and share reproducible environments for data science development.
In this chapter you'll create an Anaconda Project, which is a data science asset that specifies package installs, file downloads, and executable commands. Anaconda projects can be used to run Jupyter notebooks, Bokeh server apps, REST APIs, and command line tools on Windows, Mac OSX, and Linux platforms making deployment easy.
Anaconda projects are shared amongst data scientists as compressed directories containing the Conda environment specification, URLs for downloadable files, and source code for commands. Collaboration can be achieved through text file revision control tools, like git.
In the following exercises you'll learn how to create, run, and share Anaconda Projects.
Which of the following CANNOT be achieved with an Anaconda Project?