Beyond Jupyter Notebooks - Building your own Data Science platform with Python & Docker

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Interactive notebooks like Jupyter have become more and more popular inthe recent past and build the core of many data scientist's workplace.Being accessed via web browser they allow scientists to easily structuretheir work by combining code and documentation.Yet notebooks often lead to isolated and disposable analysis artefacts.Keeping the computation inside those notebooks does not allow forconvenient concurrent model training, model exposure or scheduled modelretraining.Those issues can be addressed by taking advantage of recent developmentsin the discipline of software engineering. Over the past yearscontainerization became the technology of choice for crafting anddeploying applications. Building a data science platform that allows foreasy access (via notebooks), flexibility and reproducibility (viacontainerization) combines the best of both worlds and addresses DataScientist's hidden needs.

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