About

AI for Software Engineers started as my forcing function to stay current with technical developments in AI.

Since then, it’s done the same for tens of thousands of engineers.

I explain research and engineering topics necessary to get AI into production. I focus on technical depth and use my experience to go deep across the entire AI stack.

Hi, I’m Logan 👋

I work on Google’s Machine Learning Platform for Ads where I build infrastructure, agents, and developer tooling to make building AI easier. Previously, I built ML infra at Microsoft and researched machine learning applications in medicine.

Why you should sign up

AI is the next era of software engineering. It has fundamentally changed the way engineers build systems and solve problems. By understanding AI and remaining cutting edge, you can multiply the impact you have and the problems you can solve.

Need further convincing?

Here’s what other AI authors and subscribers say about AI for Software Engineers:

  • “Logan is a long-time ML engineer that does an awesome job of explaining the intersection of ML/engineering (and beyond) in a digestible manner.” - Cameron R. Wolfe, Ph.D., Author of Deep (Learning) Focus

  • “Logan from [AI for Software Engineers] is doing an amazing job demystifying machine learning!” - Sahar Mor, Author of AI Tidbits

  • “A solid mix of AI news and ML engineering insights, written by an ML engineer at Google.” - MLOps Community

  • “Some of the most insightful AI perspectives and technical expertise from a prominent Google data scientist. (Though the Spider-Man shirt? Still on the fence about that one. đŸ€·)” - Sergei Polevikov, Author of AI Health Uncut

  • “Accessible tech deep drives and commentary from a smart Google ML engineer. It’s often important to get under the hood for newer concepts in AI, and this newsletter helps with that.” - James Wang, Author of Weighty Thoughts

To get started now, check out my top articles on the home page.

If you’d like to contact me, I’m best reached via email.

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What you need to know to bring AI to production. Spans machine learning systems, research, infrastructure, and more with a focus on application.

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