We (https://illumidesk.com) support Julia notebooks (leveraging Jupyter Server Kernels using a variety of frontends, such as JupyterLab, VS Code, etc). The Robotics department (taught by Professor Jessy Grizzle at the University of Michigan) uses Julia extensively. Over the course of the last three years we have learned a few things about Julia when compared to Python and friends:
- Julia is faster at reading CSV's compared to using something like Pandas (presumably because it's multi-threaded and Python is single threaded) - Julia does seem faster with read operations, but it's important to write code that declares types (myIntArray = Int[] vs myIntArray = []), this way Julia would read from a contiguous block of memory instead of reading an array of pointers in memory - Julia's packages are limited to Python's equivalents
Our take is that Julia is amazing for ML/DL but Python is better for entry level data scientists. Once the data scientist gets up to speed with Python then Julia is a nice feather in the cap for more specialized use cases.