Bridging the gap between ML theory and practice
One of the most peculiar things about Machine Learning is the dichotomy between academic ML research and industrial ML production. Taking a glance at the flood of ML papers on arXiv, it seems that a new breakthrough is happening almost every week. Yet, how exactly we use these algorithms as part of an ML production system, which not only includes the ML model, but also a way to turn the model scores into meaningful business actions and ensure that these actions are the rights ones, is much less documented.
When I first started writing about Machine Learning and Data Science back in 2019, my goal has been to close this academic/industrial gap. Since then, I’ve published dozens of articles on the subject, and my audience has grown to over 2K followers, including ML students as well as ML practitioners. I published all of my writing on the online publishing platform Medium, and this book is a curated selection of 13 of my best pieces.
If you want to have a permanent, curated selection of my best articles (without the need for a monthly Medium subscription), this e-book is for you.
This book is organized in 4 themes,
- ML system design - how ML systems are designed
- ML operations - how ML systems are operated, monitored, and tested
- Advanced ML topics - class imbalance, BERT, and reinforcement learning, and
- Beyond ML - thoughts about the future of Machine Learning.
About the author
I've been working on ML research and applications since 2018, first as a postdoctoral researcher at Argonne National Lab, then as data scientist at JP Morgan Chase, then as Applied Scientist at Amazon, and then as ML Engineer at Meta. My background is in Physics.
What you'll get
A single pdf file with 50 pages packed with useful ML insights from my best articles. Table of contents:
1. ML system design
- Learning to rank: A primer
- People You May Know: Behind the Algorithms That Bring Users Together
- The four maturity levels of ML production systems
2. ML operations
- The Joy of A/B Testing: Theory, Practice, and Pitfalls
- The Joy of A/B Testing, Part II: Advanced Topics
- Deploying Your Machine Learning Model Is Just the Beginning
- Is My Model Really Better? Why ML models that look good on paper are not guaranteed to work well in production
3. Advanced ML topics
- Class Imbalance in Machine Learning Problems: A Practical Guide
- What exactly happens when we fine-tune BERT?
- Reinforcement Learning: Machines that learn by doing
4. Beyond Machine Learning
- Algorithms are not enough
- Machine Learning: Science or Alchemy?
- The limits of knowledge
You'll get a pdf file with 50 pages packed with useful ML insights from 13 of my best articles