I have a series of posts on DevOps for Data Science where I am covering a set of concepts for a DevOps “Maturity Model” – a list of things you can do, in order, that will set you on the path for implementing DevOps in Data Science. In this article, I’ll cover the next maturity…
Year: 2017
DevOps for Data Science – Continuous Integration
In the previous post in this series on DevOps for Data Science, I covered the first the concept in a DevOps “Maturity Model” – a list of things you can do, in order, that will set you on the path for implementing DevOps in Data Science. The first thing you can do in your projects…
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…
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…
DevOps for Data Science – DevOps isn’t the Toolchain (But you still have to care about the tech)
In this series on DevOps for Data Science, I’m covering the Team Data Science Process, a definition of DevOps, and why Data Scientists need to think about DevOps. It’s interesting that most definitions of DevOps deal more with what it isn’t, than what it is. Mine, as you recall, is quite simple: DevOps is including…
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…
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…
The Keys to Effective Data Science Projects – Part 10: Project Close-Out with the TDSP
Data Science projects have a lot in common with other IT projects in general, and with Business Intelligence in particular. There are differences, however, and I’ve covered those for you here in this series on The Keys to Effective Data Science Projects. One of those areas where general projects and Data Science projects are similar…
The Keys to Effective Data Science Projects – Part 9: Testing and Validation
We’re continuing our discussion of the series of the Keys to Effective Data Science Projects, this time focusing on Testing and Validating the Model. We’re in the general phase in the Team Data Science Process called “Customer Acceptance“. “Testing” in the general sense is the same in Data Science projects and any other typical software project – it’s ensuring…
The Keys to Effective Data Science Projects – Part 8: Operationalize
We’re in part eight on our journey through the series of the Keys to Effective Data Science Projects –“Operationalization” – a term only a marketer could love. It really just means “people using your solution”. And it’s this part of the process that is quite possibly the most complicated, and usually the one done with the…