DevOps for Data Science – Infrastructure as Code

In the previous post in this series on DevOps for Data Science, I explained that it’s often difficult to try and implement all of the DevOps practices and tools at one time. I introduced the concept of a “Maturity Model” – a list of things you can do, in order, that will set you on…

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DevOps for Data Science – DevOps Maturity

In this series on DevOps for Data Science, I’ve explained what DevOps is, and given you lots of resources to go learn more about it. Now we can get to the details of implementing DevOps in your Data Science Projects. Consider that the standard Software Development Lifecycle (SDLC) with Data Science algorithms or API’s added…

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DevOps for Data Science – Defining DevOps

I’m wading into treacherous waters here. Computing terms often defy explanation, especially newer ones. While “DevOps” or Developer Operations has been around for a while, it’s still not as mature a term as, say, “Relational Database Management System (RDBMS)”. That term is well known, understood, and accepted. (It wasn’t when it came out). Whatever definition…

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DevOps for Data Science – Who needs it?

Data Scientists have often worked in a bit of a “silo” – meaning they were off to the side in an organization, maybe not even part of the Information Technology (IT) function. But that is changing. As data science projects are adopted into the mainstream, there is a need for structure. I’ve explained a modern…

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