Steph Locke explains the CRISP-DM model for data mining projects and applies it to data science projects:

Within a given project, we know that at the beginning of our first ever project we may not have a lot of domain knowledge, or there might be problems with the data or the model might not be valuable enough to put into production. These things happen, and the really nice thing about the CRISP-DM model is it allows for us to do that. It’s not a single linear path from project kick-off to deployment. It helps you remember not to beat yourself up over having to go back a step. It also equips you with something upfront to explain to managers that sometimes you will need to bounce between some phases, and that’s ok.

This is another place in which “iterate, iterate, iterate” ends up being the best answer available.

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Wrapping Up A Data Science Project

I have finished my series on launching a data science project.  First, I have a post on deploying models as microservices: The other big shift is a shift away from single, large services which try to solve all of the problems.  Instead, we’ve entered the era of the microservice:  a small service dedicated to providing […]

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