Collection of references, paper, articles that useful when building ML Products
- Awesome production machine learning https://github.com/EthicalML/awesome-production-machine-learning#privacy-preserving-machine-learning
- MLOps Stack Template https://valohai.com/blog/the-mlops-stack/
- Feast (Feature Store) https://docs.feast.dev/
- Meet Michelangelo: Uber’s Machine Learning Platform (2017) https://eng.uber.com/michelangelo-machine-learning-platform/
- Scaling Machine Learning at Uber with Michelangelo https://eng.uber.com/scaling-michelangelo/
- Hidden Technical Debt in Machine Learning Systems https://papers.nips.cc/paper/2015/file/86df7dcfd896fcaf2674f757a2463eba-Paper.pdf
- Label Studio: Open Source Data annotation tool https://github.com/heartexlabs/label-studio
- Snorkel: Programmatically build training data https://github.com/snorkel-team/snorkel
- How to Measure Quality when Training Machine Learning Models https://labelbox.com/blog/how-to-measure-quality-when-training-machine-learning-models/
- A Visual Guide to Using BERT for the First Time http://jalammar.github.io/a-visual-guide-to-using-bert-for-the-first-time/
- How AI is getting better at detecting hate speech https://ai.facebook.com/blog/how-ai-is-getting-better-at-detecting-hate-speech/
- Andrew Ng's Coursera Machine Learning Course https://www.coursera.org/learn/machine-learning
- The Missing Semester of Your CS Education https://missing.csail.mit.edu/
- AI Expert Roadmap https://i.am.ai/roadmap/
- OpenDS4All https://github.com/odpi/OpenDS4All
- Open ML Curriculum https://docs.google.com/document/d/1w9pRuThemeu3uycslvv3R9wZEbeFNYLz0XwFO4D00tA/edit
- Thinking Fast and Slow
- Mythical Man Month
- Managing the Unmanageable: Rules, Tools, and Insights for Managing Software People and Teams