Machine learning is complex. Building software is also complex. When you build systems with machine learning 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 learning it, 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 Machine Learning 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.” -
,“Logan from [Machine Learning for Software Engineers] 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?
I write about everything important for a software engineer to know about machine learning. This includes:
Contextualizing AI news: There’s a lot more to what’s happening in the world of AI than most people know. I use my experience to contextualize current events and explain the why behind them. I don’t focus on hype or bullshit—just reality.
Interesting jobs, the job market, and job skills: I share jobs I’ve found that you’ll find interesting, updates about the current state of the job market, and the most important skills to make you hireable.
Practical hands-on guides for building real AI: I build real AI systems and share how I do it. This usually starts from a premise (something we want done) and walks through how machine learning can achieve it.
Machine learning terminology explanations: One of the hardest parts about breaking into ML is understanding the terminology. I explain the terms you should know and what they mean.
Resources to help you be a better engineer: I’m always on the lookout for resources to improve. I share these with you.
Machine learning case studies: I dive into how a company uses AI and how it impacts their bottom line.
Crossposts and collaborations I find important.
Roundups to keep you up-to-date when you’re short on time.
I promise at least one article a week (but probably more). Paid subscribers get more of the above. If you find Machine Learning for Software Engineers helpful, you can support it for just $3/mo and get everything.
Who finds Machine Learning for Software Engineers 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.
But I’m not a software engineer…
Don’t worry! Everything I write here is explained clearly for a broader audience. You don’t need to follow code examples or work with the hands-on tutorials to get something from the newsletter—you’ll just get more practical experience when you do!
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If you’re unsure about joining, check out some of my favorite articles:
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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 Machine Learning for Software Engineers 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
