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

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