“It works on my machine”. There can be lots of friction when developing, deploying and running code in a production system. The discrepancy between environments can lead to non-deterministic results. Docker puts all your applications along with their environment into containers so they can easily be shipped to production, solving a whole range of other problems, such as reliability, scalability, reproducibility.
This course takes a hands-on approach to teach the essentials of Docker, with focus on the data scientist’s workflow. You will create your own Dockerfiles, build docker images and spin-up some containers, so that you can feel confident to say, that your analytics results are reproducible by other data scientists and that your predictive models are easy to deploy.