DevOps for Data Science – Load Testing and Auto-Scale

In this series on DevOps for Data Science, I’ve explained the concept of 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 final DevOps Maturity Model  is Load Testing and Auto-Scale. Note that you want to … Continue reading DevOps for Data Science – Load Testing and Auto-Scale

DevOps for Data Science – Application Performance Monitoring

In this series on DevOps for Data Science, I’ve explained the concept of 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 is to implement Infrastructure as Code … Continue reading DevOps for Data Science – Application Performance Monitoring

DevOps for Data Science – Continuous Delivery

In this series on DevOps for Data Science, I’ve explained the concept of 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 is to implement Infrastructure as Code … Continue reading DevOps for Data Science – Continuous Delivery

DevOps for Data Science – Automated Testing

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 … Continue reading DevOps for Data Science – Automated Testing

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 … Continue reading DevOps for Data Science – Continuous Integration

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 … Continue reading DevOps for Data Science – Infrastructure as Code

DevOps for Data Science – DevOps Maturity

In this series on DevOps for Data Science, I 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 in looks something like … Continue reading DevOps for Data Science – DevOps Maturity

DevOps for Data Science – DevOps isn’t the Toolchain (But you still have to care about the tech)

DevOps for Data Science –DevOps isn’t the Toolchain 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 … Continue reading DevOps for Data Science – DevOps isn’t the Toolchain (But you still have to care about the tech)

DevOps for Data Science – Defining DevOps

I’m wading into treacherous waters here in my series on DevOps for Data Science. 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. … Continue reading DevOps for Data Science – Defining DevOps