Machine learning is complex. Building software is also complex. When you build systems with machine learning (AI) you get the complexity of both plus a little extra. Thus, applying AI to real-world problems is really complex. That’s what I’m interested in.
👋 Hi, I’m Logan. I build continuous machine learning infrastructure at Google. I’ve also worked on ML infra at Microsoft and trained ML models to segment medical images and reduce medial imaging costs. I work as an engineer during the day and write about machine learning engineering at night.
I want all software engineers to realize that building with AI is the future and, with a wee bit of math and a little bit of guidance, 100% doable. So I write no-nonsense, hands-on technical guides to explain machine learning engineering concepts. My goal is to help you gain the confidence to build really cool things with AI.
Why should I subscribe?
Here’s what subscribers say about Society’s Backend:
“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.” -
,“Logan from Society's Backend is doing an amazing job demystifying machine learning!” -
,“I never miss an issue. A great source of information, tools and updates.” -
,“A solid mix of AI news and ML engineering insights, written by an ML engineer at Google.” -
“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. 🤷)” -
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And more:
What I can expect?
One weekly article detailing important happenings, resources, and discussion points from the past week in AI to contextualize what’s going. These keep you updated on the things engineers should know while ignoring the hype found on social media.
Other articles going further in-depth into MLE topics. I like to explain these in multiple formats with visuals and code examples (basically whatever makes it easier for readers to understand). I release these whenever I feel like it because I never want them to be rushed.
Monthly articles that go in-depth into bonus topics. The bonus topics are reading lists from the past month, an accurate overview of the current state of the AI job market, and ML case studies. These are the extra topics that are important to know and go beyond MLE basics. These are specifically for paid subscribers to thank them for their support. If you find it helpful, you can support Society’s Backend for just $3/mo.
Who finds Society’s Backend helpful?
Software engineers wanting to learn ML engineering.
AI veterans wanting to stay up-to-date on AI news and understand why it’s important.
AI enthusiasts wanting a realistic understanding of the complexities of bring AI to real-world products.
Article to get started
If you’re unsure about joining, check out some of my favorite articles:
and many other topics.
Other resources
I’ve also written a streamlined road map to learn ML fundamentals for free.
If newsletters aren’t your thing, I totally understand! You can also treat Society’s Backend as a blog and find me on your favorite social platform:
We also have a community Chat here on Substack for subscribers.
Never hesitate to reach out. I’d love to chat. 😊
Always be (machine) learning,
Logan
