This is the repository of ML Systems, good resources to update yourself.
- https://stanford-cs329s.github.io/syllabus.html
- https://www.youtube.com/watch?v=OEiNnfdxBRE&list=PLSrTvUm384I9PV10koj_cqit9OfbJXEkq
- http://patrickhalina.com/posts/ml-eng-interview-guide/
- Production ML systems Coursera (https://www.coursera.org/learn/gcp-production-ml-systems?utm_source=gg&utm_medium=sem&utm_content=01-CatalogDSA-ML1-US&campaignid=9918777773&adgroupid=100491712477&device=c&keyword=&matchtype=b&network=g&devicemodel=&adpostion=&creativeid=432357975999&hide_mobile_promo&gclid=EAIaIQobChMI9KW1tve_8AIVC67ICh1pCgUOEAAYAiAAEgJX6_D_BwE#about)
- Full Stack Deep Learning (https://fullstackdeeplearning.com/spring2021/)
- https://www.educative.io/courses/data-science-in-production-building-scalable-model-pipelines
- https://www.educative.io/courses/grokking-the-machine-learning-interview/7DXzZzpQQoO
- https://towardsdatascience.com/how-to-get-a-machine-learning-job-in-6-months-5aaa61b13af2
- https://mlwhiz.com/blog/2020/10/22/mle-fb-interview/
- https://towardsdatascience.com/how-to-answer-any-machine-learning-system-design-interview-question-a98656bb7ff0
- https://towardsdatascience.com/how-to-ace-ml-interview-questions-ae826387b583
- https://towardsdatascience.com/math-you-need-to-succeed-in-ml-interviews-9e717d61f296
- https://mail.google.com/mail/u/0/?zx=7e3ilkklhii9#inbox/FMfcgxwLtQTbgpxSmgNGwPdkhJvqnwQc
- https://sanglevikas25.medium.com/preparation-tips-for-interviews-at-faang-5cee4752b33e
- https://avs431.medium.com/explain-it-to-me-like-a-5-year-old-introduction-to-lstm-and-attention-models-part-2-2-16482a58b30b
- https://www.linkedin.com/posts/ameya-shanbhag_explain-it-to-me-like-a-5-year-old-introduction-activity-6776724598198173696-RNRL
- https://www.linkedin.com/posts/ameya-shanbhag_explain-it-to-me-like-a-5-year-old-deep-activity-6775199010513817600-aU2y
- https://www.linkedin.com/posts/ameya-shanbhag_explain-it-to-me-like-a-5-year-old-beginners-activity-6769314332858716160-qCqb