Merge pull request eugeneyan#183 from eugeneyan/dev

Add AB testing practices, data security, and some ML applications

README.md

@@ -63,6 +63,7 @@ P.P.S, Looking for guides and interviews on applying ML? 👉[`applyingML`](http
 4. [How DoorDash is Scaling its Data Platform to Delight Customers and Meet Growing Demand](https://doordash.engineering/2020/09/25/how-doordash-is-scaling-its-data-platform/) `DoorDash`
 5. [Revolutionizing Money Movements at Scale with Strong Data Consistency](https://eng.uber.com/money-scale-strong-data/) `Uber`
 6. [Zipline - A Declarative Feature Engineering Framework](https://www.youtube.com/watch?v=LjcKCm0G_OY) `Airbnb`
+7. [Automating Data Protection at Scale, Part 1](https://medium.com/airbnb-engineering/automating-data-protection-at-scale-part-1-c74909328e08) ([Part 2](https://medium.com/airbnb-engineering/automating-data-protection-at-scale-part-2-c2b8d2068216)) `Airbnb`
 7. [Real-time Data Infrastructure at Uber](https://arxiv.org/pdf/2104.00087.pdf) `Uber`
 
 
@@ -209,6 +210,7 @@ P.P.S, Looking for guides and interviews on applying ML? 👉[`applyingML`](http
 2. [Why Do People Buy Seemingly Irrelevant Items in Voice Product Search?](https://www.amazon.science/publications/why-do-people-buy-irrelevant-items-in-voice-product-search) ([Paper](https://assets.amazon.science/f7/48/0562b2c14338a0b76ccf4f523fa5/why-do-people-buy-irrelevant-items-in-voice-product-search.pdf)) `Amazon`
 3. [Semantic Product Search](https://arxiv.org/abs/1907.00937) ([Paper](https://arxiv.org/pdf/1907.00937.pdf)) `Amazon`
 4. [QUEEN: Neural query rewriting in e-commerce](https://www.amazon.science/publications/queen-neural-query-rewriting-in-e-commerce) ([Paper](https://assets.amazon.science/f9/78/dda8f1e143dba8ca96e43ec487c6/queen-neural-query-rewriting-in-ecommerce.pdf)) `Amazon`
+5. [Using Learning-to-rank to Precisely Locate Where to Deliver Packages](https://www.amazon.science/blog/using-learning-to-rank-to-precisely-locate-where-to-deliver-packages) ([Paper](https://www.amazon.science/publications/getting-your-package-to-the-right-place-supervised-machine-learning-for-geolocation)) `Amazon`
 3. [How Lazada Ranks Products to Improve Customer Experience and Conversion](https://www.slideshare.net/eugeneyan/how-lazada-ranks-products-to-improve-customer-experience-and-conversion) `Lazada`
 4. [Using Deep Learning at Scale in Twitter’s Timelines](https://blog.twitter.com/engineering/en_us/topics/insights/2017/using-deep-learning-at-scale-in-twitters-timelines.html) `Twitter`
 5. [Machine Learning-Powered Search Ranking of Airbnb Experiences](https://medium.com/airbnb-engineering/machine-learning-powered-search-ranking-of-airbnb-experiences-110b4b1a0789) `Airbnb`
@@ -430,6 +432,8 @@ P.P.S, Looking for guides and interviews on applying ML? 👉[`applyingML`](http
 11. [It’s All A/Bout Testing: The Netflix Experimentation Platform](https://netflixtechblog.com/its-all-a-bout-testing-the-netflix-experimentation-platform-4e1ca458c15) `Netflix`
 11. [Computational Causal Inference at Netflix](https://netflixtechblog.com/computational-causal-inference-at-netflix-293591691c62) ([Paper](https://arxiv.org/pdf/2007.10979.pdf)) `Netflix`
 12. [Key Challenges with Quasi Experiments at Netflix](https://netflixtechblog.com/key-challenges-with-quasi-experiments-at-netflix-89b4f234b852) `Netflix`
+13. [Interpreting A/B Test Results: False Positives and Statistical Significance](https://netflixtechblog.com/interpreting-a-b-test-results-false-positives-and-statistical-significance-c1522d0db27a) `Netflix`
+13. [Interpreting A/B Test Results: False Negatives and Power](https://netflixtechblog.com/interpreting-a-b-test-results-false-negatives-and-power-6943995cf3a8) `Netflix`
 13. [Constrained Bayesian Optimization with Noisy Experiments](https://research.fb.com/publications/constrained-bayesian-optimization-with-noisy-experiments/) ([Paper](https://arxiv.org/pdf/1706.07094.pdf)) `Facebook`
 16. [Detecting Interference: An A/B Test of A/B Tests](https://engineering.linkedin.com/blog/2019/06/detecting-interference--an-a-b-test-of-a-b-tests) `LinkedIn`
 15. [Making the LinkedIn experimentation engine 20x faster](https://engineering.linkedin.com/blog/2020/making-the-linkedin-experimentation-engine-20x-faster) `LinkedIn`
@@ -441,6 +445,7 @@ P.P.S, Looking for guides and interviews on applying ML? 👉[`applyingML`](http
 18. [Leveraging Causal Modeling to Get More Value from Flat Experiment Results](https://doordash.engineering/2020/09/18/causal-modeling-to-get-more-value-from-flat-experiment-results/) `DoorDash`
 25. [Iterating Real-time Assignment Algorithms Through Experimentation](https://doordash.engineering/2020/12/08/optimizing-real-time-algorithms-experimentation/) `DoorDash`
 25. [Running Experiments with Google Adwords for Campaign Optimization](https://doordash.engineering/2021/02/05/google-adwords-campaign-optimization/) `DoorDash`
+26. [The 4 Principles DoorDash Used to Increase Its Logistics Experiment Capacity by 1000%](https://doordash.engineering/2021/09/21/the-4-principles-doordash-used-to-increase-its-logistics-experiment-capacity-by-1000/)
 18. [Spotify’s New Experimentation Platform (Part 1)](https://engineering.atspotify.com/2020/10/29/spotifys-new-experimentation-platform-part-1/) [(Part 2)](https://engineering.atspotify.com/2020/11/02/spotifys-new-experimentation-platform-part-2/) `Spotify`
 19. [Overlapping Experiment Infrastructure: More, Better, Faster Experimentation](https://research.google/pubs/pub36500/) ([Paper](https://storage.googleapis.com/pub-tools-public-publication-data/pdf/36500.pdf)) `Google`
 20. [Experimentation Platform at Zalando: Part 1 - Evolution](https://engineering.zalando.com/posts/2021/01/experimentation-platform-part1.html) `Zalando`
@@ -483,7 +488,7 @@ P.P.S, Looking for guides and interviews on applying ML? 👉[`applyingML`](http
 9. [Core Modeling at Instagram](https://instagram-engineering.com/core-modeling-at-instagram-a51e0158aa48) `Instagram`
 10. [Open-Sourcing Metaflow - a Human-Centric Framework for Data Science](https://netflixtechblog.com/open-sourcing-metaflow-a-human-centric-framework-for-data-science-fa72e04a5d9) `Netflix`
 11. [MLOps at GreenSteam: Shipping Machine Learning](https://neptune.ai/blog/mlops-at-greensteam-shipping-machine-learning-case-study) `GreenSteam`
-
+12. [Evolving Reddit’s ML Model Deployment and Serving Architecture](https://www.reddit.com/r/RedditEng/comments/q14tsw/evolving_reddits_ml_model_deployment_and_serving/) `Reddit`
 
 ## Practices
 1. [Practical Recommendations for Gradient-Based Training of Deep Architectures](https://arxiv.org/abs/1206.5533) ([Paper](https://arxiv.org/pdf/1206.5533.pdf)) `Yoshua Bengio`
@@ -501,12 +506,15 @@ P.P.S, Looking for guides and interviews on applying ML? 👉[`applyingML`](http
 12. [Maintaining Machine Learning Model Accuracy Through Monitoring](https://doordash.engineering/2021/05/20/monitor-machine-learning-model-drift/) `DoorDash`
 13. [Building Scalable and Performant Marketing ML Systems at Wayfair](https://www.aboutwayfair.com/careers/tech-blog/building-scalable-and-performant-marketing-ml-systems-at-wayfair) `Wayfair`
 14. [Our approach to building transparent and explainable AI systems](https://engineering.linkedin.com/blog/2021/transparent-and-explainable-AI-systems) `LinkedIn`
+15. [5 Steps for Building Machine Learning Models for Business](https://shopify.engineering/building-business-machine-learning-models) `Shopify`
 
 ## Team structure
 1. [Engineers Shouldn’t Write ETL: A Guide to Building a High Functioning Data Science Department](https://multithreaded.stitchfix.com/blog/2016/03/16/engineers-shouldnt-write-etl/) `Stitch Fix`
 2. [Beware the Data Science Pin Factory: The Power of the Full-Stack Data Science Generalist](https://multithreaded.stitchfix.com/blog/2019/03/11/FullStackDS-Generalists/) `Stitch Fix`
 3. [Cultivating Algorithms: How We Grow Data Science at Stitch Fix](https://cultivating-algos.stitchfix.com) `Stitch Fix`
 4. [Analytics at Netflix: Who We Are and What We Do](https://netflixtechblog.com/analytics-at-netflix-who-we-are-and-what-we-do-7d9c08fe6965) `Netflix`
+5. [Building a Data Team at a Mid-stage Startup: A Short Story](https://erikbern.com/2021/07/07/the-data-team-a-short-story.html) `Erikbern`
+6. [Building The Analytics Team At Wish](https://medium.com/wish-engineering/scaling-analytics-at-wish-619eacb97d16) `Wish`
 
 ## Fails
 1. [160k+ High School Students Will Graduate Only If a Model Allows Them to](http://positivelysemidefinite.com/2020/06/160k-students.html) `International Baccalaureate`