MLOps - One Dev Question video series

Over the coming weeks, we'll be releasing a series of short "One Dev Question" videos talking about MLOps and surrounding topics.

MLOps - One Dev Question series intro video

As they get released, I'll be releasing accompanying blog posts exploring the ideas behind each video in more depth, and providing links for further information. You can find the currently-released posts below:

The complete list of posts:

  1. What is Machine Learning?
  2. What's the difference between Machine Learning and Artificial Intelligence?
  3. What is MLOps?
  4. What is AIOps and how does it differ from MLOps?
  5. What DevOps practices can be applied to Machine Learning projects?
  6. How does MLOps differ from DevOps?
  7. What parts of an ML project should be in source control?
  8. Can I use DevOps tooling for Machine Learning projects?
  9. Why should I care about MLOps?
  10. When should I think about MLOps?
  11. How can MLOps improve my predictive models?
  12. What is Azure Machine Learning?

MLOps (or DevOps for Machine Learning) is a new and sometimes confusing field. It can be hard to pin down exactly what's involved, who's responsible for what, and the relationship with traditional software delivery practices.

I'm pretty excited about this series! The videos are all around 2 minutes or less, and I've tried to really drill into the key points.

Hopefully the short, digestible videos help explain some of the buzzwords, and encourage you to find out more. I'm looking forward to hearing what you think!

Damian Brady

I'm an Australian developer, speaker, and author specialising in DevOps, MLOps, developer process, and software architecture. I love Azure DevOps, GitHub Actions, and reducing process waste.