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

Ethics and the Importance of Being an Information Skeptic

Whenever I teach or present a session on Artificial Intelligence, I start with Ethics. We've created a site where you can quickly walk through a few of the major principles we follow at Microsoft for AI here: http://aka.ms/ai-ethics. I walk through these principles before I show how to design a Machine Learning solution, and then … Continue reading Ethics and the Importance of Being an Information Skeptic

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

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 … Continue reading The Keys to Effective Data Science Projects – Part 10: Project Close-Out with the TDSP

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 - … Continue reading The Keys to Effective Data Science Projects – Part 9: Testing and Validation

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 … Continue reading The Keys to Effective Data Science Projects – Part 8: Operationalize