Distributed Systems Reading List
Introduction I often argue that the toughest thing about distributed systems is changing the way you think. The below is a collection of material I've found useful for motivating these changes. Thought Provokers Ramblings that make you think about the way you design. Not everything can be solved with big servers, databases and transactions. Harvest, Yield and Scalable Tolerant Systems - Real world
GitHub - eugeneyan/applied-ml: 📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.
Curated papers, articles, and blogs on data science & machine learning in production. ⚙️ Figuring out how to implement your ML project? Learn how other organizations did it: How the problem is framed 🔎(e.g., personalization as recsys vs. search vs. sequences) What machine learning techniques worked ✅ (and sometimes, what didn't ❌) Why it works, the science behind it with research, literature, and
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