Recently I had an ML system design interview and while I am waiting for the results I want to share the resources I found very helpful for my preparation: \n\n1. [Facebook Field Guide to ML](https://research.fb.com/blog/2018/05/the-facebook-field-guide-to-machine-learning-video-series/). This is a very important resource to internalize, it is concise yet quite deep and full of non-trivial examples and concepts. \n\n2. ["The hundred-page Machine Learning book"](https://github.com/tirthajyoti/Papers-Literature-ML-DL-RL-AI/blob/master/General-Machine-Learning/The%20Hundred-Page%20Machine%20Learning%20Book%20by%20Andriy%20Burkov/Links%20to%20read%20the%20chapters%20online.md) by Andriy Burkov. A perfect book in case you want to refresh your core ML knowledge. \n\n3. [Google crash ML course](https://developers.google.com/machine-learning/crash-course). This course is also great to solidify your knowledge of fundamental ML principles with the full-scale industrial projects in mind. \n\n4. Two installments of ML systems from UC Berkeley. The videos are not available, unfortunately; but you can look thought the syllabus and study the readings required for the course:\n- [Machine Learning Systems](https://ucbrise.github.io/cs294-ai-sys-sp19/) (Spring 2019)\n- [Machine Learning Systems](https://ucbrise.github.io/cs294-ai-sys-fa19/) (Fall 2019)\n\n5. [Machine Learning system design](https://github.com/chiphuyen/machine-learning-systems-design/blob/master/build/build1/consolidated.pdf) on GitHub. A nice description of how to structure your answer and what to keep in mind during the actual interview.  \n\nGood luck! \n\n\n\n

Recently I had an ML system design interview and while I am waiting for the results I want to share the resources I found very helpful for my preparation: \n\n1