From the course: Docker for Data Scientists
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Sharing results with colleagues
From the course: Docker for Data Scientists
Sharing results with colleagues
- [Instructor] Now in data science, it's really important that others are able to reproduce our findings. So we might send our data out to colleagues for them to check the work that we've done, or we might be dealing with healthcare data where it's really important that our results can be confirmed, because work can be audited. Now as machine learning engineers or data scientists, one of the things that you use a lot are Jupyter Notebooks. Now one of the problems you often have is that something works on your computer, but it doesn't work on someone else's. Now that's no good, and this is why Docker is so great. It gets rid of the problem, because we ship everything together. We send the notebook, we send the software that's required, and the data. So let's say I've put together a notebook to show a summary of the total medals won by participating countries in the 2008 Olympics. Now if you're interested, I've taken both this dataset and the notebook from my Python Pandas course on…
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