A New Chapter:: Continuing onward to MLOps
Designing Machine Learning Systems (MLOPs) Review Begins!
Image: The scenery of New York City taken by me.
After several weeks (actually, it was several months, though) of consistent posting, I’m wrapping up my TIL25_StatReview series, which focused on revisiting the foundations of statistics and probability. This journey sharpened my intuition and reconnected me to the mathematical backbone of modern machine learning. From revisiting central limit theorems to power laws in error analysis, every post helped reinforce concepts I had once taken for granted.
📚 With that chapter now concluded, I’m excited to continue posting challenges with a brand-new learning journey centered around the book Designing Machine Learning Systems by Chip Huyen.
This book is more than just a technical manual—it’s a field guide for anyone serious about building ML systems & MLOps that work in the real world. While academic models are often clean and bounded, real production systems are anything but. This is where the book shines:
- It explores how to design, deploy, and maintain machine learning (ML) systems end-to-end.
- It touches on often-overlooked aspects like data collection strategy, feedback loops, monitoring, CI/CD, and system iteration.
- It blends MLOps best practices with product thinking, emphasizing that building a model is just a small part of building a successful system.
🚀 Over the coming weeks, I’ll be summarizing key lessons from each chapter while layering in my reflections, primarily through the lens of real-time NLP systems, which I’ve been actively developing.
The TIL25_MLOps series will focus on:
- End-to-end ML system lifecycle
- Trade-offs in system design
- Scalable architecture patterns
- Real-world failure modes and debugging
- Building maintainable, reliable, and adaptable ML pipelines
If you’re interested in bridging the gap between modeling and deployment or want a peek into how production-grade ML is done, this series is for you.
Let the journey begin! ✨
Wonha Shin
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