Deep Dive Into Containerization for Data Science & Machine Learning

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This is a summary of a well-detailed article I originally published with For complete understanding and further details, you can find the full write-up by clicking the Full article link on

When building data science and machine learning-powered products the research-development-production workflow is not linear like in traditional software development where the specs are known and problems are (mostly) understood beforehand.

There are lots of trial and error involved, including the test and use of new algorithms, trying new data versions (and managing it), packaging the product for production, end-users views and perspectives, feedback loops, and more. …


Bamigbade Opeyemi

Data Scientist |ML-Engineer at Data Science Nigeria. Open to consulting and new opportunities.

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