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Title: GitHub - FerMatPy/applied-ml: 📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production. · GitHub

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Description: 📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production. - FerMatPy/applied-ml

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https://twitter.com/eugeneyan/status/1350509546133811200
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ml-surveyshttps://github.com/eugeneyan/ml-surveys
applyingMLhttps://applyingml.com
Data Qualityhttps://github.com/FerMatPy/applied-ml#data-quality
Data Engineeringhttps://github.com/FerMatPy/applied-ml#data-engineering
Data Discoveryhttps://github.com/FerMatPy/applied-ml#data-discovery
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Reinforcement Learninghttps://github.com/FerMatPy/applied-ml#reinforcement-learning
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Team Structurehttps://github.com/FerMatPy/applied-ml#team-structure
Failshttps://github.com/FerMatPy/applied-ml#fails
https://github.com/FerMatPy/applied-ml#data-quality
Monitoring Data Quality at Scale with Statistical Modelinghttps://eng.uber.com/monitoring-data-quality-at-scale/
An Approach to Data Quality for Netflix Personalization Systemshttps://databricks.com/session_na20/an-approach-to-data-quality-for-netflix-personalization-systems
Automating Large-Scale Data Quality Verificationhttps://www.amazon.science/publications/automating-large-scale-data-quality-verification
Paperhttps://assets.amazon.science/a6/88/ad858ee240c38c6e9dce128250c0/automating-large-scale-data-quality-verification.pdf
Meet Hodor — Gojek’s Upstream Data Quality Toolhttps://www.gojek.io/blog/meet-hodor-gojeks-upstream-data-quality-tool
Reliable and Scalable Data Ingestion at Airbnbhttps://www.slideshare.net/HadoopSummit/reliable-and-scalable-data-ingestion-at-airbnb-63920989
Data Management Challenges in Production Machine Learninghttps://research.google/pubs/pub46178/
Paperhttps://thodrek.github.io/CS839_spring18/papers/p1723-polyzotis.pdf
Improving Accuracy By Certainty Estimation of Human Decisions, Labels, and Ratershttps://research.fb.com/blog/2020/08/improving-the-accuracy-of-community-standards-enforcement-by-certainty-estimation-of-human-decisions/
Paperhttps://research.fb.com/wp-content/uploads/2020/08/CLARA-Confidence-of-Labels-and-Raters.pdf
https://github.com/FerMatPy/applied-ml#data-engineering
Zipline: Airbnb’s Machine Learning Data Management Platformhttps://databricks.com/session/zipline-airbnbs-machine-learning-data-management-platform
Sputnik: Airbnb’s Apache Spark Framework for Data Engineeringhttps://databricks.com/session_na20/sputnik-airbnbs-apache-spark-framework-for-data-engineering
Unbundling Data Science Workflows with Metaflow and AWS Step Functionshttps://netflixtechblog.com/unbundling-data-science-workflows-with-metaflow-and-aws-step-functions-d454780c6280
How DoorDash is Scaling its Data Platform to Delight Customers and Meet Growing Demandhttps://doordash.engineering/2020/09/25/how-doordash-is-scaling-its-data-platform/
Revolutionizing Money Movements at Scale with Strong Data Consistencyhttps://eng.uber.com/money-scale-strong-data/
Zipline - A Declarative Feature Engineering Frameworkhttps://www.youtube.com/watch?v=LjcKCm0G_OY
Real-time Data Infrastructure at Uberhttps://arxiv.org/pdf/2104.00087.pdf
https://github.com/FerMatPy/applied-ml#data-discovery
Amundsen — Lyft’s Data Discovery & Metadata Enginehttps://eng.lyft.com/amundsen-lyfts-data-discovery-metadata-engine-62d27254fbb9
Open Sourcing Amundsen: A Data Discovery And Metadata Platformhttps://eng.lyft.com/open-sourcing-amundsen-a-data-discovery-and-metadata-platform-2282bb436234
Codehttps://github.com/lyft/amundsen
Amundsen: One Year Laterhttps://eng.lyft.com/amundsen-1-year-later-7b60bf28602
Using Amundsen to Support User Privacy via Metadata Collection at Squarehttps://developer.squareup.com/blog/using-amundsen-to-support-user-privacy-via-metadata-collection-at-square/
Discovery and Consumption of Analytics Data at Twitterhttps://blog.twitter.com/engineering/en_us/topics/insights/2016/discovery-and-consumption-of-analytics-data-at-twitter.html
Democratizing Data at Airbnbhttps://medium.com/airbnb-engineering/democratizing-data-at-airbnb-852d76c51770
Databook: Turning Big Data into Knowledge with Metadata at Uberhttps://eng.uber.com/databook/
Turning Metadata Into Insights with Databookhttps://eng.uber.com/metadata-insights-databook/
Metacat: Making Big Data Discoverable and Meaningful at Netflixhttps://netflixtechblog.com/metacat-making-big-data-discoverable-and-meaningful-at-netflix-56fb36a53520
Codehttps://github.com/Netflix/metacat
Exploring Data @ Netflixhttps://netflixtechblog.com/exploring-data-netflix-9d87e20072e3
DataHub: A Generalized Metadata Search & Discovery Toolhttps://engineering.linkedin.com/blog/2019/data-hub
Codehttps://github.com/linkedin/datahub
DataHub: Popular Metadata Architectures Explainedhttps://engineering.linkedin.com/blog/2020/datahub-popular-metadata-architectures-explained
How We Improved Data Discovery for Data Scientists at Spotifyhttps://engineering.atspotify.com/2020/02/27/how-we-improved-data-discovery-for-data-scientists-at-spotify/
How We’re Solving Data Discovery Challenges at Shopifyhttps://engineering.shopify.com/blogs/engineering/solving-data-discovery-challenges-shopify
Nemo: Data discovery at Facebookhttps://engineering.fb.com/data-infrastructure/nemo/
Apache Atlas: Data Goverance and Metadata Framework for Hadoophttps://atlas.apache.org/#/
Codehttps://github.com/apache/atlas
Collect, Aggregate, and Visualize a Data Ecosystem's Metadatahttps://marquezproject.github.io/marquez/
Codehttps://github.com/MarquezProject/marquez
Exploring Data at Netflixhttps://netflixtechblog.com/exploring-data-netflix-9d87e20072e3
Codehttps://github.com/Netflix/nf-data-explorer
https://github.com/FerMatPy/applied-ml#feature-stores
Introducing Feast: An Open Source Feature Store for Machine Learninghttps://cloud.google.com/blog/products/ai-machine-learning/introducing-feast-an-open-source-feature-store-for-machine-learning
Codehttps://github.com/feast-dev/feast
Feast: Bridging ML Models and Datahttps://www.gojek.io/blog/feast-bridging-ml-models-and-data
Building a Scalable ML Feature Store with Redis, Binary Serialization, and Compressionhttps://doordash.engineering/2020/11/19/building-a-gigascale-ml-feature-store-with-redis/
Building Riviera: A Declarative Real-Time Feature Engineering Frameworkhttps://doordash.engineering/2021/03/04/building-a-declarative-real-time-feature-engineering-framework/
Michelangelo Palette: A Feature Engineering Platform at Uberhttps://www.infoq.com/presentations/michelangelo-palette-uber/
Optimal Feature Discovery: Better, Leaner Machine Learning Models Through Information Theoryhttps://eng.uber.com/optimal-feature-discovery-ml/
Distributed Time Travel for Feature Generationhttps://netflixtechblog.com/distributed-time-travel-for-feature-generation-389cccdd3907
Fact Store at Scale for Netflix Recommendationshttps://databricks.com/session/fact-store-scale-for-netflix-recommendations
The Architecture That Powers Twitter's Feature Storehttps://www.youtube.com/watch?v=UNailXoiIrY
Building the Activity Graph, Part 2 (Feature Storage Section)https://engineering.linkedin.com/blog/2017/07/building-the-activity-graph--part-2
Rapid Experimentation Through Standardization: Typed AI features for LinkedIn’s Feedhttps://engineering.linkedin.com/blog/2020/feed-typed-ai-features
Accelerating Machine Learning with the Feature Store Servicehttps://technology.condenast.com/story/accelerating-machine-learning-with-the-feature-store-service
Building a Feature Storehttps://nlathia.github.io/2020/12/Building-a-feature-store.html
Zipline: Airbnb’s Machine Learning Data Management Platformhttps://databricks.com/session/zipline-airbnbs-machine-learning-data-management-platform
ML Feature Serving Infrastructure at Lyfthttps://eng.lyft.com/ml-feature-serving-infrastructure-at-lyft-d30bf2d3c32a
Butterfree: A Spark-based Framework for Feature Store Buildinghttps://medium.com/quintoandar-tech-blog/butterfree-a-spark-based-framework-for-feature-store-building-48c3640522c7
Codehttps://github.com/quintoandar/butterfree
https://github.com/FerMatPy/applied-ml#classification
High-Precision Phrase-Based Document Classification on a Modern Scalehttps://engineering.linkedin.com/research/2011/high-precision-phrase-based-document-classification-on-a-modern-scale
Paperhttp://web.stanford.edu/~gavish/documents/phrase_based.pdf
Chimera: Large-scale Classification using Machine Learning, Rules, and Crowdsourcinghttps://dl.acm.org/doi/10.14778/2733004.2733024
Paperhttp://pages.cs.wisc.edu/%7Eanhai/papers/chimera-vldb14.pdf
Deep Learning: Product Categorization and Shelvinghttps://medium.com/walmartglobaltech/deep-learning-product-categorization-and-shelving-630571e81e96
Large-scale Item Categorization for e-Commercehttps://dl.acm.org/doi/10.1145/2396761.2396838
Paperhttps://www.researchgate.net/profile/Jean_David_Ruvini/publication/262270957_Large-scale_item_categorization_for_e-commerce/links/5512dc3d0cf270fd7e33a0d5/Large-scale-item-categorization-for-e-commerce.pdf
Large-scale Item Categorization in e-Commerce Using Multiple Recurrent Neural Networkshttps://www.kdd.org/kdd2016/subtopic/view/large-scale-item-categorization-in-e-commerce-using-multiple-recurrent-neur/
Paperhttps://www.kdd.org/kdd2016/papers/files/adf0392-haAemb.pdf
Categorizing Products at Scalehttps://engineering.shopify.com/blogs/engineering/categorizing-products-at-scale
Learning to Diagnose with LSTM Recurrent Neural Networkshttps://arxiv.org/abs/1511.03677
Paperhttps://arxiv.org/pdf/1511.03677.pdf
Discovering and Classifying In-app Message Intent at Airbnbhttps://medium.com/airbnb-engineering/discovering-and-classifying-in-app-message-intent-at-airbnb-6a55f5400a0c
How We Built the Good First Issues Featurehttps://github.blog/2020-01-22-how-we-built-good-first-issues/
Teaching Machines to Triage Firefox Bugshttps://hacks.mozilla.org/2019/04/teaching-machines-to-triage-firefox-bugs/
Testing Firefox More Efficiently with Machine Learninghttps://hacks.mozilla.org/2020/07/testing-firefox-more-efficiently-with-machine-learning/
Using ML to Subtype Patients Receiving Digital Mental Health Interventionshttps://www.microsoft.com/en-us/research/blog/a-path-to-personalization-using-ml-to-subtype-patients-receiving-digital-mental-health-interventions/
Paperhttps://jamanetwork.com/journals/jamanetworkopen/fullarticle/2768347
Prediction of Advertiser Churn for Google AdWordshttps://research.google/pubs/pub36678/
Paperhttps://storage.googleapis.com/pub-tools-public-publication-data/pdf/36678.pdf
Scalable Data Classification for Security and Privacyhttps://engineering.fb.com/security/data-classification-system/
Paperhttps://arxiv.org/pdf/2006.14109.pdf
Uncovering Online Delivery Menu Best Practices with Machine Learninghttps://doordash.engineering/2020/11/10/uncovering-online-delivery-menu-best-practices-with-machine-learning/
Using a Human-in-the-Loop to Overcome the Cold Start Problem in Menu Item Tagginghttps://doordash.engineering/2020/08/28/overcome-the-cold-start-problem-in-menu-item-tagging/
https://github.com/FerMatPy/applied-ml#regression
Using Machine Learning to Predict Value of Homes On Airbnbhttps://medium.com/airbnb-engineering/using-machine-learning-to-predict-value-of-homes-on-airbnb-9272d3d4739d
Using Machine Learning to Predict the Value of Ad Requestshttps://blog.twitter.com/engineering/en_us/topics/insights/2020/using-machine-learning-to-predict-the-value-of-ad-requests.html
Open-Sourcing Riskquant, a Library for Quantifying Riskhttps://netflixtechblog.com/open-sourcing-riskquant-a-library-for-quantifying-risk-6720cc1e4968
Codehttps://github.com/Netflix-Skunkworks/riskquant
Solving for Unobserved Data in a Regression Model Using a Simple Data Adjustmenthttps://doordash.engineering/2020/10/14/solving-for-unobserved-data-in-a-regression-model/
https://github.com/FerMatPy/applied-ml#forecasting
Forecasting at Uber: An Introductionhttps://eng.uber.com/forecasting-introduction/
Engineering Extreme Event Forecasting at Uber with RNNhttps://eng.uber.com/neural-networks/
Transforming Financial Forecasting with Data Science and Machine Learning at Uberhttps://eng.uber.com/transforming-financial-forecasting-machine-learning/
Under the Hood of Gojek’s Automated Forecasting Toolhttps://www.gojek.io/blog/under-the-hood-of-gojeks-automated-forecasting-tool
BusTr: Predicting Bus Travel Times from Real-Time Traffichttps://dl.acm.org/doi/abs/10.1145/3394486.3403376
Paperhttps://dl.acm.org/doi/pdf/10.1145/3394486.3403376
Videohttps://crossminds.ai/video/5f3369790576dd25aef288db/
Retraining Machine Learning Models in the Wake of COVID-19https://doordash.engineering/2020/09/15/retraining-ml-models-covid-19/
Managing Supply and Demand Balance Through Machine Learninghttps://doordash.engineering/2021/06/29/managing-supply-and-demand-balance-through-machine-learning/
Automatic Forecasting using Prophet, Databricks, Delta Lake and MLflowhttps://www.youtube.com/watch?v=TkcpjnLh690
Paperhttps://peerj.com/preprints/3190.pdf
Codehttps://github.com/facebook/prophet
Greykite: A flexible, intuitive, and fast forecasting libraryhttps://engineering.linkedin.com/blog/2021/greykite--a-flexible--intuitive--and-fast-forecasting-library
https://github.com/FerMatPy/applied-ml#recommendation
Amazon.com Recommendations: Item-to-Item Collaborative Filteringhttps://ieeexplore.ieee.org/document/1167344
Paperhttps://www.cs.umd.edu/~samir/498/Amazon-Recommendations.pdf
Temporal-Contextual Recommendation in Real-Timehttps://www.amazon.science/publications/temporal-contextual-recommendation-in-real-time
Paperhttps://assets.amazon.science/96/71/d1f25754497681133c7aa2b7eb05/temporal-contextual-recommendation-in-real-time.pdf
P-Companion: A Framework for Diversified Complementary Product Recommendationhttps://www.amazon.science/publications/p-companion-a-principled-framework-for-diversified-complementary-product-recommendation
Paperhttps://assets.amazon.science/d5/16/3f7809974a899a11bacdadefdf24/p-companion-a-principled-framework-for-diversified-complementary-product-recommendation.pdf
Recommending Complementary Products in E-Commerce Push Notificationshttps://arxiv.org/abs/1707.08113
Paperhttps://arxiv.org/pdf/1707.08113.pdf
Deep Interest with Hierarchical Attention Network for Click-Through Rate Predictionhttps://arxiv.org/abs/2005.12981
Paperhttps://arxiv.org/pdf/2005.12981.pdf
Behavior Sequence Transformer for E-commerce Recommendation in Alibabahttps://arxiv.org/abs/1905.06874
Paperhttps://arxiv.org/pdf/1905.06874.pdf
TPG-DNN: A Method for User Intent Prediction with Multi-task Learninghttps://arxiv.org/abs/2008.02122
Paperhttps://arxiv.org/pdf/2008.02122.pdf
PURS: Personalized Unexpected Recommender System for Improving User Satisfactionhttps://dl.acm.org/doi/10.1145/3383313.3412238
Paperhttps://dl.acm.org/doi/pdf/10.1145/3383313.3412238
SDM: Sequential Deep Matching Model for Online Large-scale Recommender Systemhttps://arxiv.org/abs/1909.00385
Paperhttps://arxiv.org/pdf/1909.00385.pdf
Multi-Interest Network with Dynamic Routing for Recommendation at Tmallhttps://arxiv.org/abs/1904.08030
Paperhttps://arxiv.org/pdf/1904.08030.pdf
Controllable Multi-Interest Framework for Recommendationhttps://arxiv.org/abs/2005.09347
Paperhttps://arxiv.org/pdf/2005.09347
MiNet: Mixed Interest Network for Cross-Domain Click-Through Rate Predictionhttps://arxiv.org/abs/2008.02974
Paperhttps://arxiv.org/pdf/2008.02974.pdf
ATBRG: Adaptive Target-Behavior Relational Graph Network for Effective Recommendationhttps://arxiv.org/abs/2005.12002
Paperhttps://arxiv.org/pdf/2005.12002.pdf
Session-based Recommendations with Recurrent Neural Networkshttps://arxiv.org/abs/1511.06939
Paperhttps://arxiv.org/pdf/1511.06939.pdf
How 20th Century Fox uses ML to predict a movie audiencehttps://cloud.google.com/blog/products/ai-machine-learning/how-20th-century-fox-uses-ml-to-predict-a-movie-audience
Paperhttps://arxiv.org/abs/1810.08189
Deep Neural Networks for YouTube Recommendationshttps://static.googleusercontent.com/media/research.google.com/en//pubs/archive/45530.pdf
Personalized Recommendations for Experiences Using Deep Learninghttps://www.tripadvisor.com/engineering/personalized-recommendations-for-experiences-using-deep-learning/
E-commerce in Your Inbox: Product Recommendations at Scalehttps://arxiv.org/abs/1606.07154
Product Recommendations at Scalehttps://arxiv.org/abs/1606.07154
Paperhttps://arxiv.org/pdf/1606.07154.pdf
Powered by AI: Instagram’s Explore recommender systemhttps://ai.facebook.com/blog/powered-by-ai-instagrams-explore-recommender-system/
Netflix Recommendations: Beyond the 5 stars (Part 1https://netflixtechblog.com/netflix-recommendations-beyond-the-5-stars-part-1-55838468f429
Part 2https://netflixtechblog.com/netflix-recommendations-beyond-the-5-stars-part-2-d9b96aa399f5
Learning a Personalized Homepagehttps://netflixtechblog.com/learning-a-personalized-homepage-aa8ec670359a
Artwork Personalization at Netflixhttps://netflixtechblog.com/artwork-personalization-c589f074ad76
To Be Continued: Helping you find shows to continue watching on Netflixhttps://netflixtechblog.com/to-be-continued-helping-you-find-shows-to-continue-watching-on-7c0d8ee4dab6
Calibrated Recommendationshttps://dl.acm.org/doi/10.1145/3240323.3240372
Paperhttps://dl.acm.org/doi/pdf/10.1145/3240323.3240372
Marginal Posterior Sampling for Slate Banditshttps://www.ijcai.org/proceedings/2019/308
Paperhttps://www.ijcai.org/proceedings/2019/0308.pdf
Food Discovery with Uber Eats: Recommending for the Marketplacehttps://eng.uber.com/uber-eats-recommending-marketplace/
Food Discovery with Uber Eats: Using Graph Learning to Power Recommendationshttps://eng.uber.com/uber-eats-graph-learning/
How Music Recommendation Works — And Doesn’t Workhttps://notes.variogr.am/2012/12/11/how-music-recommendation-works-and-doesnt-work/
Music recommendation at Spotifyhttp://sigir.org/afirm2019/slides/16.%20Friday%20-%20Music%20Recommendation%20at%20Spotify%20-%20Ben%20Carterette.pdf
Recommending Music on Spotify with Deep Learninghttps://benanne.github.io/2014/08/05/spotify-cnns.html
For Your Ears Only: Personalizing Spotify Home with Machine Learninghttps://engineering.atspotify.com/2020/01/16/for-your-ears-only-personalizing-spotify-home-with-machine-learning/
Reach for the Top: How Spotify Built Shortcuts in Just Six Monthshttps://engineering.atspotify.com/2020/04/15/reach-for-the-top-how-spotify-built-shortcuts-in-just-six-months/
Explore, Exploit, and Explain: Personalizing Explainable Recommendations with Banditshttps://dl.acm.org/doi/10.1145/3240323.3240354
Paperhttps://static1.squarespace.com/static/5ae0d0b48ab7227d232c2bea/t/5ba849e3c83025fa56814f45/1537755637453/BartRecSys.pdf
Contextual and Sequential User Embeddings for Large-Scale Music Recommendationhttps://dl.acm.org/doi/10.1145/3383313.3412248
Paperhttps://dl.acm.org/doi/pdf/10.1145/3383313.3412248
The Evolution of Kit: Automating Marketing Using Machine Learninghttps://engineering.shopify.com/blogs/engineering/evolution-kit-automating-marketing-machine-learning
Using Machine Learning to Predict what File you Need Next (Part 1)https://dropbox.tech/machine-learning/content-suggestions-machine-learning
Using Machine Learning to Predict what File you Need Next (Part 2)https://dropbox.tech/machine-learning/using-machine-learning-to-predict-what-file-you-need-next-part-2
Personalized Recommendations in LinkedIn Learninghttps://engineering.linkedin.com/blog/2016/12/personalized-recommendations-in-linkedin-learning
A Closer Look at the AI Behind Course Recommendations on LinkedIn Learning (Part 1)https://engineering.linkedin.com/blog/2020/course-recommendations-ai-part-one
A Closer Look at the AI Behind Course Recommendations on LinkedIn Learning (Part 2)https://engineering.linkedin.com/blog/2020/course-recommendations-ai-part-two
Learning to be Relevant: Evolution of a Course Recommendation Systemhttps://dl.acm.org/doi/pdf/10.1145/3357384.3357817
Building a Heterogeneous Social Network Recommendation Systemhttps://engineering.linkedin.com/blog/2020/building-a-heterogeneous-social-network-recommendation-system
How TikTok recommends videos #ForYouhttps://newsroom.tiktok.com/en-us/how-tiktok-recommends-videos-for-you
A Meta-Learning Perspective on Cold-Start Recommendations for Itemshttps://papers.nips.cc/paper/7266-a-meta-learning-perspective-on-cold-start-recommendations-for-items
Paperhttps://papers.nips.cc/paper/7266-a-meta-learning-perspective-on-cold-start-recommendations-for-items.pdf
Lessons Learned Addressing Dataset Bias in Model-Based Candidate Generationhttps://arxiv.org/abs/2105.09293
Paperhttps://arxiv.org/pdf/2105.09293.pdf
Zero-Shot Heterogeneous Transfer Learning from RecSys to Cold-Start Search Retrievalhttps://arxiv.org/abs/2008.02930
Paperhttps://arxiv.org/pdf/2008.02930.pdf
Improved Deep & Cross Network for Feature Cross Learning in Web-scale LTR Systemshttps://arxiv.org/abs/2008.13535
Paperhttps://arxiv.org/pdf/2008.13535.pdf
Self-supervised Learning for Large-scale Item Recommendationshttps://arxiv.org/abs/2007.12865
Paperhttps://arxiv.org/pdf/2007.12865.pdf
Mixed Negative Sampling for Learning Two-tower Neural Networks in Recommendationshttps://research.google/pubs/pub50257/
Paperhttps://storage.googleapis.com/pub-tools-public-publication-data/pdf/b9f4e78a8830fe5afcf2f0452862fb3c0d6584ea.pdf
Personalized Channel Recommendations in Slackhttps://slack.engineering/personalized-channel-recommendations-in-slack/
Deep Retrieval: End-to-End Learnable Structure Model for Large-Scale Recommendationshttps://arxiv.org/abs/2007.07203
Paperhttps://arxiv.org/pdf/2007.07203.pdf
Future Data Helps Training: Modeling Future Contexts for Session-based Recommendationhttps://github.com/FerMatPy/applied-ml/blob/main
Paperhttps://arxiv.org/pdf/1906.04473.pdf
Using AI to Help Health Experts Address the COVID-19 Pandemichttps://ai.facebook.com/blog/using-ai-to-help-health-experts-address-the-covid-19-pandemic/
A Case Study of Session-based Recommendations in the Home-improvement Domainhttps://dl.acm.org/doi/10.1145/3383313.3412235
Paperhttps://dl.acm.org/doi/pdf/10.1145/3383313.3412235
Balancing Relevance and Discovery to Inspire Customers in the IKEA Apphttps://dl.acm.org/doi/10.1145/3383313.3411550
Paperhttps://dl.acm.org/doi/pdf/10.1145/3383313.3411550
Pixie: A System for Recommending 3+ Billion Items to 200+ Million Users in Real-Timehttps://arxiv.org/abs/1711.07601
Paperhttps://arxiv.org/pdf/1711.07601.pdf
How we use AutoML, Multi-task learning and Multi-tower models for Pinterest Adshttps://medium.com/pinterest-engineering/how-we-use-automl-multi-task-learning-and-multi-tower-models-for-pinterest-ads-db966c3dc99e
Multi-task Learning for Related Products Recommendations at Pinteresthttps://medium.com/pinterest-engineering/multi-task-learning-for-related-products-recommendations-at-pinterest-62684f631c12
Improving the Quality of Recommended Pins with Lightweight Rankinghttps://medium.com/pinterest-engineering/improving-the-quality-of-recommended-pins-with-lightweight-ranking-8ff5477b20e3
Advertiser Recommendation Systems at Pinteresthttps://medium.com/pinterest-engineering/advertiser-recommendation-systems-at-pinterest-ccb255fbde20
Personalized Cuisine Filter Based on Customer Preference and Local Popularityhttps://doordash.engineering/2020/01/27/personalized-cuisine-filter/
How We Built a Matchmaking Algorithm to Cross-Sell Productshttps://www.gojek.io/blog/how-we-built-a-matchmaking-algorithm-to-cross-sell-products
https://github.com/FerMatPy/applied-ml#search--ranking
Amazon Search: The Joy of Ranking Productshttps://www.amazon.science/publications/amazon-search-the-joy-of-ranking-products
Paperhttps://assets.amazon.science/89/cd/34289f1f4d25b5857d776bdf04d5/amazon-search-the-joy-of-ranking-products.pdf
Videohttps://www.youtube.com/watch?v=NLrhmn-EZ88
Codehttps://github.com/dariasor/TreeExtra
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
Paperhttps://assets.amazon.science/f7/48/0562b2c14338a0b76ccf4f523fa5/why-do-people-buy-irrelevant-items-in-voice-product-search.pdf
Semantic Product Searchhttps://arxiv.org/abs/1907.00937
Paperhttps://arxiv.org/pdf/1907.00937.pdf
QUEEN: Neural query rewriting in e-commercehttps://www.amazon.science/publications/queen-neural-query-rewriting-in-e-commerce
Paperhttps://assets.amazon.science/f9/78/dda8f1e143dba8ca96e43ec487c6/queen-neural-query-rewriting-in-ecommerce.pdf
How Lazada Ranks Products to Improve Customer Experience and Conversionhttps://www.slideshare.net/eugeneyan/how-lazada-ranks-products-to-improve-customer-experience-and-conversion
Using Deep Learning at Scale in Twitter’s Timelineshttps://blog.twitter.com/engineering/en_us/topics/insights/2017/using-deep-learning-at-scale-in-twitters-timelines.html
Machine Learning-Powered Search Ranking of Airbnb Experienceshttps://medium.com/airbnb-engineering/machine-learning-powered-search-ranking-of-airbnb-experiences-110b4b1a0789
Applying Deep Learning To Airbnb Searchhttps://arxiv.org/abs/1810.09591
Paperhttps://arxiv.org/pdf/1810.09591.pdf
Managing Diversity in Airbnb Searchhttps://arxiv.org/abs/2004.02621
Paperhttps://arxiv.org/pdf/2004.02621.pdf
Improving Deep Learning for Airbnb Searchhttps://arxiv.org/abs/2002.05515
Paperhttps://arxiv.org/pdf/2002.05515.pdf
Ranking Relevance in Yahoo Searchhttps://www.kdd.org/kdd2016/subtopic/view/ranking-relevance-in-yahoo-search
Paperhttps://www.kdd.org/kdd2016/papers/files/adf0361-yinA.pdf
An Ensemble-based Approach to Click-Through Rate Prediction for Promoted Listings at Etsyhttps://arxiv.org/abs/1711.01377
Paperhttps://arxiv.org/pdf/1711.01377.pdf
Learning to Rank Personalized Search Results in Professional Networkshttps://arxiv.org/abs/1605.04624
Paperhttps://arxiv.org/pdf/1605.04624.pdf
Entity Personalized Talent Search Models with Tree Interaction Featureshttps://arxiv.org/abs/1902.09041
Paperhttps://arxiv.org/pdf/1902.09041.pdf
In-session Personalization for Talent Searchhttps://arxiv.org/abs/1809.06488
Paperhttps://arxiv.org/pdf/1809.06488.pdf
The AI Behind LinkedIn Recruiter Search and recommendation systemshttps://engineering.linkedin.com/blog/2019/04/ai-behind-linkedin-recruiter-search-and-recommendation-systems
Learning Hiring Preferences: The AI Behind LinkedIn Jobshttps://engineering.linkedin.com/blog/2019/02/learning-hiring-preferences--the-ai-behind-linkedin-jobs
Quality Matches Via Personalized AI for Hirer and Seeker Preferenceshttps://engineering.linkedin.com/blog/2020/quality-matches-via-personalized-ai
Understanding Dwell Time to Improve LinkedIn Feed Rankinghttps://engineering.linkedin.com/blog/2020/understanding-feed-dwell-time
Ads Allocation in Feed via Constrained Optimizationhttps://dl.acm.org/doi/abs/10.1145/3394486.3403391
Paperhttps://dl.acm.org/doi/pdf/10.1145/3394486.3403391
Videohttps://crossminds.ai/video/5f33697a0576dd25aef288ea/
Talent Search and Recommendation Systems at LinkedInhttps://arxiv.org/abs/1809.06481
Paperhttps://arxiv.org/pdf/1809.06481.pdf
Understanding Dwell Time to Improve LinkedIn Feed Rankinghttps://engineering.linkedin.com/blog/2020/understanding-feed-dwell-time
AI at Scale in Binghttps://blogs.bing.com/search/2020_05/AI-at-Scale-in-Bing
Query Understanding Engine in Traveloka Universal Searchhttps://medium.com/traveloka-engineering/query-understanding-engine-in-traveloka-universal-search-410ad3895db7
The Secret Sauce Behind Search Personalisationhttps://www.gojek.io/blog/the-secret-sauce-behind-search-personalisation
Food Discovery with Uber Eats: Building a Query Understanding Enginehttps://eng.uber.com/uber-eats-query-understanding/
Neural Code Search: ML-based Code Search Using Natural Language Querieshttps://ai.facebook.com/blog/neural-code-search-ml-based-code-search-using-natural-language-queries/
Bayesian Product Ranking at Wayfairhttps://tech.wayfair.com/data-science/2020/01/bayesian-product-ranking-at-wayfair
COLD: Towards the Next Generation of Pre-Ranking Systemhttps://arxiv.org/abs/2007.16122
Paperhttps://arxiv.org/pdf/2007.16122.pdf
Globally Optimized Mutual Influence Aware Ranking in E-Commerce Searchhttps://arxiv.org/abs/1805.08524
Paperhttps://arxiv.org/pdf/1805.08524.pdf
Graph Intention Network for Click-through Rate Prediction in Sponsored Searchhttps://arxiv.org/abs/2103.16164
Paperhttps://arxiv.org/pdf/2103.16164.pdf
Reinforcement Learning to Rank in E-Commerce Search Enginehttps://arxiv.org/abs/1803.00710
Paperhttps://arxiv.org/pdf/1803.00710.pdf
Aggregating Search Results from Heterogeneous Sources via Reinforcement Learninghttps://arxiv.org/abs/1902.08882
Paperhttps://arxiv.org/pdf/1902.08882.pdf
Cross-domain Attention Network with Wasserstein Regularizers for E-commerce Searchhttps://dl.acm.org/doi/10.1145/3357384.3357809
Understanding Searches Better Than Ever Beforehttps://www.blog.google/products/search/search-language-understanding-bert/
Paperhttps://arxiv.org/pdf/1810.04805.pdf
Shop The Look: Building a Large Scale Visual Shopping System at Pinteresthttps://dl.acm.org/doi/abs/10.1145/3394486.3403372
Paperhttps://dl.acm.org/doi/pdf/10.1145/3394486.3403372
Videohttps://crossminds.ai/video/5f3369790576dd25aef288d7/
Driving Shopping Upsells from Pinterest Searchhttps://medium.com/pinterest-engineering/driving-shopping-upsells-from-pinterest-search-d06329255402
GDMix: A Deep Ranking Personalization Frameworkhttps://engineering.linkedin.com/blog/2020/gdmix--a-deep-ranking-personalization-framework
Codehttps://github.com/linkedin/gdmix
Bringing Personalized Search to Etsyhttps://codeascraft.com/2020/10/29/bringing-personalized-search-to-etsy/
Building a Better Search Engine for Semantic Scholarhttps://medium.com/ai2-blog/building-a-better-search-engine-for-semantic-scholar-ea23a0b661e7
Query Understanding for Natural Language Enterprise Searchhttps://arxiv.org/abs/2012.06238
Paperhttps://arxiv.org/pdf/2012.06238.pdf
How We Used Semantic Search to Make Our Search 10x Smarterhttps://medium.com/tokopedia-engineering/how-we-used-semantic-search-to-make-our-search-10x-smarter-bd9c7f601821
Powering Search & Recommendations at DoorDashhttps://doordash.engineering/2017/07/06/powering-search-recommendations-at-doordash/
Things Not Strings: Understanding Search Intent with Better Recallhttps://doordash.engineering/2020/12/15/understanding-search-intent-with-better-recall/
Query Understanding for Surfacing Under-served Music Contenthttps://research.atspotify.com/publications/query-understanding-for-surfacing-under-served-music-content/
Paperhttps://labtomarket.files.wordpress.com/2020/08/cikm2020.pdf
How We Built A Context-Specific Bidding System for Etsy Adshttps://codeascraft.com/2021/03/23/how-we-built-a-context-specific-bidding-system-for-etsy-ads/
Query2vec: Search query expansion with query embeddingshttps://bytes.grubhub.com/search-query-embeddings-using-query2vec-f5931df27d79
Embedding-based Retrieval in Facebook Searchhttps://arxiv.org/abs/2006.11632
Paperhttps://arxiv.org/pdf/2006.11632.pdf
Towards Personalized and Semantic Retrieval for E-commerce Search via Embedding Learninghttps://arxiv.org/abs/2006.02282
Paperhttps://arxiv.org/pdf/2006.02282.pdf
MOBIUS: Towards the Next Generation of Query-Ad Matching in Baidu’s Sponsored Searchhttp://research.baidu.com/Public/uploads/5d12eca098d40.pdf
Pre-trained Language Model based Ranking in Baidu Searchhttps://arxiv.org/abs/2105.11108
Paperhttps://arxiv.org/pdf/2105.11108.pdf
https://github.com/FerMatPy/applied-ml#embeddings
Billion-scale Commodity Embedding for E-commerce Recommendation in Alibabahttps://arxiv.org/abs/1803.02349
Paperhttps://arxiv.org/pdf/1803.02349.pdf
Embeddings@Twitterhttps://blog.twitter.com/engineering/en_us/topics/insights/2018/embeddingsattwitter.html
Listing Embeddings in Search Rankinghttps://medium.com/airbnb-engineering/listing-embeddings-for-similar-listing-recommendations-and-real-time-personalization-in-search-601172f7603e
Paperhttps://www.kdd.org/kdd2018/accepted-papers/view/real-time-personalization-using-embeddings-for-search-ranking-at-airbnb
Understanding Latent Stylehttps://multithreaded.stitchfix.com/blog/2018/06/28/latent-style/
Towards Deep and Representation Learning for Talent Search at LinkedInhttps://arxiv.org/abs/1809.06473
Paperhttps://arxiv.org/pdf/1809.06473.pdf
Should we Embed? A Study on Performance of Embeddings for Real-Time Recommendationshttps://arxiv.org/abs/1907.06556
Paperhttps://arxiv.org/pdf/1907.06556.pdf
Vector Representation Of Items, Customer And Cart To Build A Recommendation Systemhttps://arxiv.org/abs/1705.06338
Paperhttps://arxiv.org/pdf/1705.06338.pdf
Machine Learning for a Better Developer Experiencehttps://netflixtechblog.com/machine-learning-for-a-better-developer-experience-1e600c69f36c
Announcing ScaNN: Efficient Vector Similarity Searchhttps://ai.googleblog.com/2020/07/announcing-scann-efficient-vector.html
Paperhttps://arxiv.org/pdf/1908.10396.pdf
Codehttps://github.com/google-research/google-research/tree/master/scann
Personalized Store Feed with Vector Embeddingshttps://doordash.engineering/2018/04/02/personalized-store-feed-with-vector-embeddings/
Embedding-based Retrieval at Scribdhttps://tech.scribd.com/blog/2021/embedding-based-retrieval-scribd.html
https://github.com/FerMatPy/applied-ml#natural-language-processing
Abusive Language Detection in Online User Contenthttps://dl.acm.org/doi/10.1145/2872427.2883062
Paperhttp://www.yichang-cs.com/yahoo/WWW16_Abusivedetection.pdf
How Natural Language Processing Helps LinkedIn Members Get Support Easilyhttps://engineering.linkedin.com/blog/2019/04/how-natural-language-processing-help-support
Building Smart Replies for Member Messageshttps://engineering.linkedin.com/blog/2017/10/building-smart-replies-for-member-messages
DeText: A deep NLP Framework for Intelligent Text Understandinghttps://engineering.linkedin.com/blog/2020/open-sourcing-detext
Codehttps://github.com/linkedin/detext
Smart Reply: Automated Response Suggestion for Emailhttps://research.google/pubs/pub45189/
Paperhttps://storage.googleapis.com/pub-tools-public-publication-data/pdf/45189.pdf
Gmail Smart Compose: Real-Time Assisted Writinghttps://arxiv.org/abs/1906.00080
Paperhttps://arxiv.org/pdf/1906.00080.pdf
SmartReply for YouTube Creatorshttps://ai.googleblog.com/2020/07/smartreply-for-youtube-creators.html
Using Neural Networks to Find Answers in Tableshttps://ai.googleblog.com/2020/04/using-neural-networks-to-find-answers.html
Paperhttps://arxiv.org/pdf/2004.02349.pdf
A Scalable Approach to Reducing Gender Bias in Google Translatehttps://ai.googleblog.com/2020/04/a-scalable-approach-to-reducing-gender.html
Assistive AI Makes Replying Easierhttps://www.microsoft.com/en-us/research/group/msai/articles/assistive-ai-makes-replying-easier-2/
AI Advances to Better Detect Hate Speechhttps://ai.facebook.com/blog/ai-advances-to-better-detect-hate-speech/
A State-of-the-Art Open Source Chatbothttps://ai.facebook.com/blog/state-of-the-art-open-source-chatbot
Paperhttps://arxiv.org/pdf/2004.13637.pdf
A Highly Efficient, Real-Time Text-to-Speech System Deployed on CPUshttps://ai.facebook.com/blog/a-highly-efficient-real-time-text-to-speech-system-deployed-on-cpus/
Deep Learning to Translate Between Programming Languageshttps://ai.facebook.com/blog/deep-learning-to-translate-between-programming-languages/
Paperhttps://arxiv.org/abs/2006.03511
Codehttps://github.com/facebookresearch/TransCoder
Deploying Lifelong Open-Domain Dialogue Learninghttps://arxiv.org/abs/2008.08076
Paperhttps://arxiv.org/pdf/2008.08076.pdf
Introducing Dynabench: Rethinking the way we benchmark AIhttps://ai.facebook.com/blog/dynabench-rethinking-ai-benchmarking/
Dynaboard: Moving Beyond Accuracy to Holistic Model Evaluation in NLPhttps://ai.facebook.com/blog/dynaboard-moving-beyond-accuracy-to-holistic-model-evaluation-in-nlp
Codehttps://github.com/facebookresearch/dynalab?fbclid=IwAR3qcV7QK2uXm4s4M0XUoQQo4i2DEsDy0LZFKxSQCHhP-3hF6fr2-NDFWX8
Goal-Oriented End-to-End Conversational Models with Profile Features in a Real-World Settinghttps://www.amazon.science/publications/goal-oriented-end-to-end-chatbots-with-profile-features-in-a-real-world-setting
Paperhttps://assets.amazon.science/47/03/e0d14dc34d3eb6e0d4ec282067bd/goal-oriented-end-to-end-chatbots-with-profile-features-in-a-real-world-setting.pdf
How Gojek Uses NLP to Name Pickup Locations at Scalehttps://www.gojek.io/blog/nlp-cartobert
Give Me Jeans not Shoes: How BERT Helps Us Deliver What Clients Wanthttps://multithreaded.stitchfix.com/blog/2019/07/15/give-me-jeans/
The State-of-the-art Open-Domain Chatbot in Chinese and Englishhttp://research.baidu.com/Blog/index-view?id=142
Paperhttps://arxiv.org/pdf/2006.16779.pdf
PEGASUS: A State-of-the-Art Model for Abstractive Text Summarizationhttps://ai.googleblog.com/2020/06/pegasus-state-of-art-model-for.html
Paperhttps://arxiv.org/pdf/1912.08777.pdf
Codehttps://github.com/google-research/pegasus
Photon: A Robust Cross-Domain Text-to-SQL Systemhttps://www.aclweb.org/anthology/2020.acl-demos.24/
Paperhttps://www.aclweb.org/anthology/2020.acl-demos.24.pdf
Demohttp://naturalsql.com
GeDi: A Powerful New Method for Controlling Language Modelshttps://blog.einstein.ai/gedi/
Paperhttps://arxiv.org/abs/2009.06367
Codehttps://github.com/salesforce/GeDi
Applying Topic Modeling to Improve Call Center Operationshttps://www.youtube.com/watch?v=kzRR8OjF_eI&t=2s
WIDeText: A Multimodal Deep Learning Frameworkhttps://medium.com/airbnb-engineering/widetext-a-multimodal-deep-learning-framework-31ce2565880c
Dynaboard: Moving Beyond Accuracy to Holistic Model Evaluation in NLPhttps://ai.facebook.com/blog/dynaboard-moving-beyond-accuracy-to-holistic-model-evaluation-in-nlp
How we reduced our text similarity runtime by 99.96%https://medium.com/data-science-at-microsoft/how-we-reduced-our-text-similarity-runtime-by-99-96-e8e4b4426b35
https://github.com/FerMatPy/applied-ml#sequence-modelling
Practice on Long Sequential User Behavior Modeling for Click-Through Rate Predictionhttps://arxiv.org/abs/1905.09248
Paperhttps://arxiv.org/pdf/1905.09248.pdf
Search-based User Interest Modeling with Sequential Behavior Data for CTR Predictionhttps://arxiv.org/abs/2006.05639
Paperhttps://arxiv.org/pdf/2006.05639.pdf
Deep Learning for Electronic Health Recordshttps://ai.googleblog.com/2018/05/deep-learning-for-electronic-health.html
Paperhttps://www.nature.com/articles/s41746-018-0029-1.pdf
Deep Learning for Understanding Consumer Historieshttps://engineering.zalando.com/posts/2016/10/deep-learning-for-understanding-consumer-histories.html
Paperhttps://doogkong.github.io/2017/papers/paper2.pdf
Continual Prediction of Notification Attendance with Classical and Deep Networkshttps://arxiv.org/abs/1712.07120
Paperhttps://arxiv.org/pdf/1712.07120.pdf
Using Recurrent Neural Network Models for Early Detection of Heart Failure Onsethttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5391725/
Paperhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5391725/pdf/ocw112.pdf
Doctor AI: Predicting Clinical Events via Recurrent Neural Networkshttps://arxiv.org/abs/1511.05942
Paperhttps://arxiv.org/pdf/1511.05942.pdf
How Duolingo uses AI in every part of its apphttps://venturebeat.com/2020/08/18/how-duolingo-uses-ai-in-every-part-of-its-app/
Leveraging Online Social Interactions For Enhancing Integrity at Facebookhttps://research.fb.com/blog/2020/08/leveraging-online-social-interactions-for-enhancing-integrity-at-facebook/
Paperhttps://research.fb.com/wp-content/uploads/2020/08/TIES-Temporal-Interaction-Embeddings-For-Enhancing-Social-Media-Integrity-At-Facebook.pdf
Videohttps://crossminds.ai/video/5f3369780576dd25aef288cf/
https://github.com/FerMatPy/applied-ml#computer-vision
Categorizing Listing Photos at Airbnbhttps://medium.com/airbnb-engineering/categorizing-listing-photos-at-airbnb-f9483f3ab7e3
Amenity Detection and Beyond — New Frontiers of Computer Vision at Airbnbhttps://medium.com/airbnb-engineering/amenity-detection-and-beyond-new-frontiers-of-computer-vision-at-airbnb-144a4441b72e
Powered by AI: Advancing product understanding and building new shopping experienceshttps://ai.facebook.com/blog/powered-by-ai-advancing-product-understanding-and-building-new-shopping-experiences/
New AI Research to Help Predict COVID-19 Resource Needs From X-rayshttps://ai.facebook.com/blog/new-ai-research-to-help-predict-covid-19-resource-needs-from-a-series-of-x-rays/
Paperhttps://arxiv.org/pdf/2101.04909.pdf
Modelhttps://github.com/facebookresearch/CovidPrognosis
Creating a Modern OCR Pipeline Using Computer Vision and Deep Learninghttps://dropbox.tech/machine-learning/creating-a-modern-ocr-pipeline-using-computer-vision-and-deep-learning
How we Improved Computer Vision Metrics by More Than 5% Only by Cleaning Labelling Errorshttps://deepomatic.com/en/how-we-improved-computer-vision-metrics-by-more-than-5-percent-only-by-cleaning-labelling-errors/
A Neural Weather Model for Eight-Hour Precipitation Forecastinghttps://ai.googleblog.com/2020/03/a-neural-weather-model-for-eight-hour.html
Paperhttps://arxiv.org/pdf/2003.12140.pdf
Machine Learning-based Damage Assessment for Disaster Reliefhttps://ai.googleblog.com/2020/06/machine-learning-based-damage.html
Paperhttps://arxiv.org/pdf/1910.06444.pdf
RepNet: Counting Repetitions in Videoshttps://ai.googleblog.com/2020/06/repnet-counting-repetitions-in-videos.html
Paperhttps://openaccess.thecvf.com/content_CVPR_2020/papers/Dwibedi_Counting_Out_Time_Class_Agnostic_Video_Repetition_Counting_in_the_CVPR_2020_paper.pdf
Converting Text to Images for Product Discoveryhttps://www.amazon.science/blog/converting-text-to-images-for-product-discovery
Paperhttps://assets.amazon.science/4c/76/5830542547b7a11089ce3af943b4/scipub-972.pdf
How Disney Uses PyTorch for Animated Character Recognitionhttps://medium.com/pytorch/how-disney-uses-pytorch-for-animated-character-recognition-a1722a182627
Image Captioning as an Assistive Technologyhttps://www.ibm.com/blogs/research/2020/07/image-captioning-assistive-technology/
Videohttps://ivc.ischool.utexas.edu/~yz9244/VizWiz_workshop/videos/MMTeam-oral.mp4
AI for AG: Production machine learning for agriculturehttps://medium.com/pytorch/ai-for-ag-production-machine-learning-for-agriculture-e8cfdb9849a1
AI for Full-Self Driving at Teslahttps://youtu.be/hx7BXih7zx8?t=513
On-device Supermarket Product Recognitionhttps://ai.googleblog.com/2020/07/on-device-supermarket-product.html
Using Machine Learning to Detect Deficient Coverage in Colonoscopy Screeningshttps://ai.googleblog.com/2020/08/using-machine-learning-to-detect.html
Paperhttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9097918
Shop The Look: Building a Large Scale Visual Shopping System at Pinteresthttps://dl.acm.org/doi/abs/10.1145/3394486.3403372
Paperhttps://dl.acm.org/doi/pdf/10.1145/3394486.3403372
Videohttps://crossminds.ai/video/5f3369790576dd25aef288d7/
Developing Real-Time, Automatic Sign Language Detection for Video Conferencinghttps://ai.googleblog.com/2020/10/developing-real-time-automatic-sign.html
Paperhttps://storage.googleapis.com/pub-tools-public-publication-data/pdf/2eaf0d18ec6bef00d7dd88f39dd4f9ff13eeeeb2.pdf
Vision-based Price Suggestion for Online Second-hand Itemshttps://arxiv.org/abs/2012.06009
Paperhttps://arxiv.org/pdf/2012.06009.pdf
Making machines recognize and transcribe conversations in meetings using audio and videohttps://www.microsoft.com/en-us/research/blog/making-machines-recognize-and-transcribe-conversations-in-meetings-using-audio-and-video/
An Efficient Training Approach for Very Large Scale Face Recognitionhttps://arxiv.org/abs/2105.10375
Paperhttps://arxiv.org/pdf/2105.10375
Identifying Document Types at Scribdhttps://tech.scribd.com/blog/2021/identifying-document-types.html
https://github.com/FerMatPy/applied-ml#reinforcement-learning
Deep Reinforcement Learning for Sponsored Search Real-time Biddinghttps://arxiv.org/abs/1803.00259
Paperhttps://arxiv.org/pdf/1803.00259.pdf
Dynamic Pricing on E-commerce Platform with Deep Reinforcement Learninghttps://arxiv.org/abs/1912.02572
Paperhttps://arxiv.org/pdf/1912.02572.pdf
Budget Constrained Bidding by Model-free Reinforcement Learning in Display Advertisinghttps://arxiv.org/abs/1802.08365
Paperhttps://arxiv.org/pdf/1802.08365.pdf
Productionizing Deep Reinforcement Learning with Spark and MLflowhttps://databricks.com/session_na20/productionizing-deep-reinforcement-learning-with-spark-and-mlflow
Deep Reinforcement Learning in Production Part1https://towardsdatascience.com/deep-reinforcement-learning-in-production-7e1e63471e2
Part 2https://towardsdatascience.com/deep-reinforcement-learning-in-production-part-2-personalizing-user-notifications-812a68ce2355
Building AI Trading Systemshttps://dennybritz.com/blog/ai-trading/
Reinforcement Learning for On-Demand Logisticshttps://doordash.engineering/2018/09/10/reinforcement-learning-for-on-demand-logistics/
Reinforcement Learning to Rank in E-Commerce Search Enginehttps://arxiv.org/abs/1803.00710
Paperhttps://arxiv.org/pdf/1803.00710.pdf
https://github.com/FerMatPy/applied-ml#anomaly-detection
Detecting Performance Anomalies in External Firmware Deploymentshttps://netflixtechblog.com/detecting-performance-anomalies-in-external-firmware-deployments-ed41b1bfcf46
Detecting and Preventing Abuse on LinkedIn using Isolation Forestshttps://engineering.linkedin.com/blog/2019/isolation-forest
Codehttps://github.com/linkedin/isolation-forest
Preventing Abuse Using Unsupervised Learninghttps://databricks.com/session_na20/preventing-abuse-using-unsupervised-learning
The Technology Behind Fighting Harassment on LinkedInhttps://engineering.linkedin.com/blog/2020/fighting-harassment
Uncovering Insurance Fraud Conspiracy with Network Learninghttps://arxiv.org/abs/2002.12789
Paperhttps://arxiv.org/pdf/2002.12789.pdf
How Does Spam Protection Work on Stack Exchange?https://stackoverflow.blog/2020/06/25/how-does-spam-protection-work-on-stack-exchange/
Auto Content Moderation in C2C e-Commercehttps://www.usenix.org/conference/opml20/presentation/ueta
Blocking Slack Invite Spam With Machine Learninghttps://slack.engineering/blocking-slack-invite-spam-with-machine-learning/
Cloudflare Bot Management: Machine Learning and Morehttps://blog.cloudflare.com/cloudflare-bot-management-machine-learning-and-more/
Anomalies in Oil Temperature Variations in a Tunnel Boring Machinehttps://www.youtube.com/watch?v=YV_uLLhPRAk
Using Anomaly Detection to Monitor Low-Risk Bank Customershttps://www.youtube.com/watch?v=MExokMM_Bp4&t=3s
Fighting fraud with Triplet Losshttps://tech.olx.com/fighting-fraud-with-triplet-loss-86e5f79c7a3e
Facebook is Now Using AI to Sort Content for Quicker Moderationhttps://www.theverge.com/2020/11/13/21562596/facebook-ai-moderation
Alternativehttps://venturebeat.com/2020/11/13/facebooks-redoubled-ai-efforts-wont-stop-the-spread-of-harmful-content/
Part 1https://ai.facebook.com/blog/how-ai-is-getting-better-at-detecting-hate-speech/
Part 2https://ai.facebook.com/blog/heres-how-were-using-ai-to-help-detect-misinformation/
Part 3https://ai.facebook.com/blog/training-ai-to-detect-hate-speech-in-the-real-world/
Part 4https://ai.facebook.com/blog/how-facebook-uses-super-efficient-ai-models-to-detect-hate-speech/
Deep Anomaly Detection with Spark and Tensorflowhttps://databricks.com/session_eu19/deep-anomaly-detection-from-research-to-production-leveraging-spark-and-tensorflow
(Hopsworks Videohttps://www.youtube.com/watch?v=TgXVU8DSyCQ
https://github.com/FerMatPy/applied-ml#graph
Building The LinkedIn Knowledge Graphhttps://engineering.linkedin.com/blog/2016/10/building-the-linkedin-knowledge-graph
Retail Graph — Walmart’s Product Knowledge Graphhttps://medium.com/walmartlabs/retail-graph-walmarts-product-knowledge-graph-6ef7357963bc
Food Discovery with Uber Eats: Using Graph Learning to Power Recommendationshttps://eng.uber.com/uber-eats-graph-learning/
AliGraph: A Comprehensive Graph Neural Network Platformhttps://arxiv.org/abs/1902.08730
Paperhttps://arxiv.org/pdf/1902.08730.pdf
Scaling Knowledge Access and Retrieval at Airbnbhttps://medium.com/airbnb-engineering/scaling-knowledge-access-and-retrieval-at-airbnb-665b6ba21e95
Contextualizing Airbnb by Building Knowledge Graphhttps://medium.com/airbnb-engineering/contextualizing-airbnb-by-building-knowledge-graph-b7077e268d5a
Traffic Prediction with Advanced Graph Neural Networkshttps://deepmind.com/blog/article/traffic-prediction-with-advanced-graph-neural-networks
SimClusters: Community-Based Representations for Recommendationshttps://dl.acm.org/doi/10.1145/3394486.3403370
Paperhttps://dl.acm.org/doi/pdf/10.1145/3394486.3403370
Videohttps://crossminds.ai/video/5f3369790576dd25aef288d5/
Metapaths guided Neighbors aggregated Network for Heterogeneous Graph Reasoninghttps://arxiv.org/abs/2103.06474
Paperhttps://arxiv.org/pdf/2103.06474.pdf
Graph Intention Network for Click-through Rate Prediction in Sponsored Searchhttps://arxiv.org/abs/2103.16164
Paperhttps://arxiv.org/pdf/2103.16164.pdf
JEL: Applying End-to-End Neural Entity Linking in JPMorgan Chasehttps://ojs.aaai.org/index.php/AAAI/article/view/17796
Paperhttps://www.aaai.org/AAAI21Papers/IAAI-21.DingW.pdf
Graph Convolutional Neural Networks for Web-Scale Recommender Systemshttps://arxiv.org/abs/1806.01973
Paperhttps://arxiv.org/pdf/1806.01973.pdf
https://github.com/FerMatPy/applied-ml#optimization
How Trip Inferences and Machine Learning Optimize Delivery Times on Uber Eatshttps://eng.uber.com/uber-eats-trip-optimization/
Next-Generation Optimization for Dasher Dispatch at DoorDashhttps://doordash.engineering/2020/02/28/next-generation-optimization-for-dasher-dispatch-at-doordash/
Matchmaking in Lyft Line (Part 1)https://eng.lyft.com/matchmaking-in-lyft-line-9c2635fe62c4
(Part 2)https://eng.lyft.com/matchmaking-in-lyft-line-691a1a32a008
(Part 3)https://eng.lyft.com/matchmaking-in-lyft-line-part-3-d8f9497c0e51
The Data and Science behind GrabShare Carpoolinghttps://ieeexplore.ieee.org/document/8259801
Optimization of Passengers Waiting Time in Elevators Using Machine Learninghttps://www.youtube.com/watch?v=vXndCC89BCw&t=4s
Think Out of The Package: Recommending Package Types for E-commerce Shipmentshttps://www.amazon.science/publications/think-out-of-the-package-recommending-package-types-for-e-commerce-shipments
Paperhttps://assets.amazon.science/0c/6c/9d0986b94bef92d148f0ac0da1ea/think-out-of-the-package-recommending-package-types-for-e-commerce-shipments.pdf
Optimizing DoorDash’s Marketing Spend with Machine Learninghttps://doordash.engineering/2020/07/31/optimizing-marketing-spend-with-ml/
https://github.com/FerMatPy/applied-ml#information-extraction
Unsupervised Extraction of Attributes and Their Values from Product Descriptionhttps://www.aclweb.org/anthology/I13-1190/
Paperhttps://www.aclweb.org/anthology/I13-1190.pdf
Information Extraction from Receipts with Graph Convolutional Networkshttps://nanonets.com/blog/information-extraction-graph-convolutional-networks/
Using Machine Learning to Index Text from Billions of Imageshttps://dropbox.tech/machine-learning/using-machine-learning-to-index-text-from-billions-of-images
Extracting Structured Data from Templatic Documentshttps://ai.googleblog.com/2020/06/extracting-structured-data-from.html
Paperhttps://www.aclweb.org/anthology/I13-1190.pdf
AutoKnow: self-driving knowledge collection for products of thousands of typeshttps://www.amazon.science/publications/autoknow-self-driving-knowledge-collection-for-products-of-thousands-of-types
Paperhttps://arxiv.org/pdf/2006.13473.pdf
Videohttps://crossminds.ai/video/5f3369730576dd25aef288a6/
One-shot Text Labeling using Attention and Belief Propagation for Information Extractionhttps://arxiv.org/abs/2009.04153
Paperhttps://arxiv.org/pdf/2009.04153.pdf
https://github.com/FerMatPy/applied-ml#weak-supervision
Snorkel DryBell: A Case Study in Deploying Weak Supervision at Industrial Scalehttps://dl.acm.org/doi/abs/10.1145/3299869.3314036
Paperhttps://dl.acm.org/doi/pdf/10.1145/3299869.3314036
Osprey: Weak Supervision of Imbalanced Extraction Problems without Codehttps://dl.acm.org/doi/abs/10.1145/3329486.3329492
Paperhttps://ajratner.github.io/assets/papers/Osprey_DEEM.pdf
Overton: A Data System for Monitoring and Improving Machine-Learned Productshttps://arxiv.org/abs/1909.05372
Paperhttps://arxiv.org/pdf/1909.05372.pdf
Bootstrapping Conversational Agents with Weak Supervisionhttps://www.aaai.org/ojs/index.php/AAAI/article/view/5011
Paperhttps://arxiv.org/pdf/1812.06176.pdf
https://github.com/FerMatPy/applied-ml#generation
Better Language Models and Their Implicationshttps://openai.com/blog/better-language-models/
Paperhttps://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf
Language Models are Few-Shot Learnershttps://arxiv.org/abs/2005.14165
Paperhttps://arxiv.org/pdf/2005.14165.pdf
GPT-3 Blog posthttps://openai.com/blog/openai-api/
Image GPThttps://openai.com/blog/image-gpt/
Paperhttps://cdn.openai.com/papers/Generative_Pretraining_from_Pixels_V2.pdf
Codehttps://github.com/openai/image-gpt
Deep Learned Super Resolution for Feature Film Productionhttps://graphics.pixar.com/library/SuperResolution/
Paperhttps://graphics.pixar.com/library/SuperResolution/paper.pdf
Unit Test Case Generation with Transformershttps://arxiv.org/pdf/2009.05617.pdf
https://github.com/FerMatPy/applied-ml#audio
Improving On-Device Speech Recognition with VoiceFilter-Litehttps://ai.googleblog.com/2020/11/improving-on-device-speech-recognition.html
Paperhttps://arxiv.org/pdf/2009.04323.pdf
The Machine Learning Behind Hum to Searchhttps://ai.googleblog.com/2020/11/the-machine-learning-behind-hum-to.html
https://github.com/FerMatPy/applied-ml#validation-and-ab-testing
The Reusable Holdout: Preserving Validity in Adaptive Data Analysishttps://ai.googleblog.com/2015/08/the-reusable-holdout-preserving.html
Paperhttps://science.sciencemag.org/content/sci/349/6248/636.full.pdf
Twitter Experimentation: Technical Overviewhttps://blog.twitter.com/engineering/en_us/a/2015/twitter-experimentation-technical-overview.html
Experimenting to Solve Cramminghttps://blog.twitter.com/engineering/en_us/topics/insights/2017/Experimenting-To-Solve-Cramming.html
Building an Intelligent Experimentation Platform with Uber Engineeringhttps://eng.uber.com/experimentation-platform/
Analyzing Experiment Outcomes: Beyond Average Treatment Effectshttps://eng.uber.com/analyzing-experiment-outcomes/
Under the Hood of Uber’s Experimentation Platformhttps://eng.uber.com/xp/
Announcing a New Framework for Designing Optimal Experiments with Pyrohttps://eng.uber.com/oed-pyro-release/
Paperhttps://papers.nips.cc/paper/9553-variational-bayesian-optimal-experimental-design.pdf
Paperhttps://arxiv.org/pdf/1911.00294.pdf
Enabling 10x More Experiments with Traveloka Experiment Platformhttps://medium.com/traveloka-engineering/enabling-10x-more-experiments-with-traveloka-experiment-platform-8cea13e952c
Large Scale Experimentation at Stitch Fixhttps://multithreaded.stitchfix.com/blog/2020/07/07/large-scale-experimentation/
Paperhttp://proceedings.mlr.press/v89/schmit19a/schmit19a.pdf
Multi-Armed Bandits and the Stitch Fix Experimentation Platformhttps://multithreaded.stitchfix.com/blog/2020/08/05/bandits/
Experimentation with Resource Constraintshttps://multithreaded.stitchfix.com/blog/2020/11/18/virtual-warehouse/
Modeling Conversion Rates and Saving Millions Using Kaplan-Meier and Gamma Distributionshttps://better.engineering/modeling-conversion-rates-and-saving-millions-of-dollars-using-kaplan-meier-and-gamma-distributions/
Codehttps://github.com/better/convoys
It’s All A/Bout Testing: The Netflix Experimentation Platformhttps://netflixtechblog.com/its-all-a-bout-testing-the-netflix-experimentation-platform-4e1ca458c15
Computational Causal Inference at Netflixhttps://netflixtechblog.com/computational-causal-inference-at-netflix-293591691c62
Paperhttps://arxiv.org/pdf/2007.10979.pdf
Key Challenges with Quasi Experiments at Netflixhttps://netflixtechblog.com/key-challenges-with-quasi-experiments-at-netflix-89b4f234b852
Constrained Bayesian Optimization with Noisy Experimentshttps://research.fb.com/publications/constrained-bayesian-optimization-with-noisy-experiments/
Paperhttps://arxiv.org/pdf/1706.07094.pdf
Detecting Interference: An A/B Test of A/B Testshttps://engineering.linkedin.com/blog/2019/06/detecting-interference--an-a-b-test-of-a-b-tests
Making the LinkedIn experimentation engine 20x fasterhttps://engineering.linkedin.com/blog/2020/making-the-linkedin-experimentation-engine-20x-faster
Our Evolution Towards T-REX: The Prehistory of Experimentation Infrastructure at LinkedInhttps://engineering.linkedin.com/blog/2020/our-evolution-towards-t-rex--the-prehistory-of-experimentation-i
How to Use Quasi-experiments and Counterfactuals to Build Great Productshttps://engineering.shopify.com/blogs/engineering/using-quasi-experiments-counterfactuals
Improving Experimental Power through Control Using Predictions as Covariatehttps://doordash.engineering/2020/06/08/improving-experimental-power-through-control-using-predictions-as-covariate-cupac/
Supporting Rapid Product Iteration with an Experimentation Analysis Platformhttps://doordash.engineering/2020/09/09/experimentation-analysis-platform-mvp/
Improving Online Experiment Capacity by 4X with Parallelization and Increased Sensitivityhttps://doordash.engineering/2020/10/07/improving-experiment-capacity-by-4x/
Leveraging Causal Modeling to Get More Value from Flat Experiment Resultshttps://doordash.engineering/2020/09/18/causal-modeling-to-get-more-value-from-flat-experiment-results/
Iterating Real-time Assignment Algorithms Through Experimentationhttps://doordash.engineering/2020/12/08/optimizing-real-time-algorithms-experimentation/
Running Experiments with Google Adwords for Campaign Optimizationhttps://doordash.engineering/2021/02/05/google-adwords-campaign-optimization/
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/
Overlapping Experiment Infrastructure: More, Better, Faster Experimentationhttps://research.google/pubs/pub36500/
Paperhttps://storage.googleapis.com/pub-tools-public-publication-data/pdf/36500.pdf
Experimentation Platform at Zalando: Part 1 - Evolutionhttps://engineering.zalando.com/posts/2021/01/experimentation-platform-part1.html
Scaling Airbnb’s Experimentation Platformhttps://medium.com/airbnb-engineering/https-medium-com-jonathan-parks-scaling-erf-23fd17c91166
Designing Experimentation Guardrailshttps://medium.com/airbnb-engineering/designing-experimentation-guardrails-ed6a976ec669
Reliable and Scalable Feature Toggles and A/B Testing SDK at Grabhttps://engineering.grab.com/feature-toggles-ab-testing
Meet Wasabi, an Open Source A/B Testing Platformhttps://www.intuit.com/blog/technology/engineering/meet-wasabi-an-open-source-ab-testing-platform/
Codehttps://github.com/intuit/wasabi
Building Pinterest’s A/B Testing Platformhttps://medium.com/pinterest-engineering/building-pinterests-a-b-testing-platform-ab4934ace9f4
https://github.com/FerMatPy/applied-ml#model-management
Runway - Model Lifecycle Management at Netflixhttps://www.usenix.org/conference/opml20/presentation/cepoi
Overton: A Data System for Monitoring and Improving Machine-Learned Productshttps://arxiv.org/abs/1909.05372
Paperhttps://arxiv.org/pdf/1909.05372.pdf
Managing ML Models @ Scale - Intuit’s ML Platformhttps://www.usenix.org/conference/opml20/presentation/wenzel
Operationalizing Machine Learning—Managing Provenance from Raw Data to Predictionshttps://vimeo.com/274396495
ML Model Monitoring - 9 Tips From the Trencheshttps://building.nubank.com.br/ml-model-monitoring-9-tips-from-the-trenches/
https://github.com/FerMatPy/applied-ml#efficiency
GrokNet: Unified Computer Vision Model Trunk and Embeddings For Commercehttps://ai.facebook.com/research/publications/groknet-unified-computer-vision-model-trunk-and-embeddings-for-commerce/
Paperhttps://scontent-sea1-1.xx.fbcdn.net/v/t39.8562-6/99353320_565175057533429_3886205100842024960_n.pdf?_nc_cat=110&_nc_sid=ae5e01&_nc_ohc=WQBaZy1gnmUAX8Ecqtt&_nc_ht=scontent-sea1-1.xx&oh=cab2f11dd9154d817149cb73e8b692a8&oe=5F5A3778
Permute, Quantize, and Fine-tune: Efficient Compression of Neural Networkshttps://arxiv.org/abs/2010.15703
Paperhttps://arxiv.org/pdf/2010.15703.pdf
How We Scaled Bert To Serve 1+ Billion Daily Requests on CPUshttps://blog.roblox.com/2020/05/scaled-bert-serve-1-billion-daily-requests-cpus/
https://github.com/FerMatPy/applied-ml#ethics
Building Inclusive Products Through A/B Testinghttps://engineering.linkedin.com/blog/2020/building-inclusive-products-through-a-b-testing
Paperhttps://arxiv.org/pdf/2002.05819.pdf
LiFT: A Scalable Framework for Measuring Fairness in ML Applicationshttps://engineering.linkedin.com/blog/2020/lift-addressing-bias-in-large-scale-ai-applications
Paperhttps://arxiv.org/pdf/2008.07433.pdf
https://github.com/FerMatPy/applied-ml#infra
Reengineering Facebook AI’s Deep Learning Platforms for Interoperabilityhttps://ai.facebook.com/blog/reengineering-facebook-ais-deep-learning-platforms-for-interoperability
Elastic Distributed Training with XGBoost on Rayhttps://eng.uber.com/elastic-xgboost-ray/
https://github.com/FerMatPy/applied-ml#mlops-platforms
Managing ML Models @ Scale - Intuit’s ML Platformhttps://www.usenix.org/conference/opml20/presentation/wenzel
Operationalizing Machine Learning—Managing Provenance from Raw Data to Predictionshttps://vimeo.com/274396495
Big Data Machine Learning Platform at Pinteresthttps://www.slideshare.net/Alluxio/pinterest-big-data-machine-learning-platform-at-pinterest
Real-time Machine Learning Inference Platform at Zomatohttps://www.youtube.com/watch?v=0-3ES1vzW14
Meet Michelangelo: Uber’s Machine Learning Platformhttps://eng.uber.com/michelangelo-machine-learning-platform/
Building Flexible Ensemble ML Models with a Computational Graphhttps://doordash.engineering/2021/01/26/computational-graph-machine-learning-ensemble-model-support/
LyftLearn: ML Model Training Infrastructure built on Kuberneteshttps://eng.lyft.com/lyftlearn-ml-model-training-infrastructure-built-on-kubernetes-aef8218842bb
"You Don't Need a Bigger Boat": A Full Data Pipeline Built with Open-Source Toolshttps://github.com/jacopotagliabue/you-dont-need-a-bigger-boat
Paperhttps://arxiv.org/abs/2107.07346
Core Modeling at Instagramhttps://instagram-engineering.com/core-modeling-at-instagram-a51e0158aa48
Open-Sourcing Metaflow - a Human-Centric Framework for Data Sciencehttps://netflixtechblog.com/open-sourcing-metaflow-a-human-centric-framework-for-data-science-fa72e04a5d9
https://github.com/FerMatPy/applied-ml#practices
Practical Recommendations for Gradient-Based Training of Deep Architectureshttps://arxiv.org/abs/1206.5533
Paperhttps://arxiv.org/pdf/1206.5533.pdf
Machine Learning: The High Interest Credit Card of Technical Debthttps://research.google/pubs/pub43146/
Paperhttps://storage.googleapis.com/pub-tools-public-publication-data/pdf/43146.pdf
Paperhttps://papers.nips.cc/paper/5656-hidden-technical-debt-in-machine-learning-systems.pdf
Rules of Machine Learning: Best Practices for ML Engineeringhttps://developers.google.com/machine-learning/guides/rules-of-ml
On Challenges in Machine Learning Model Managementhttp://sites.computer.org/debull/A18dec/p5.pdf
Machine Learning in Production: The Booking.com Approachhttps://booking.ai/https-booking-ai-machine-learning-production-3ee8fe943c70
150 Successful Machine Learning Models: 6 Lessons Learned at Booking.comhttps://www.kdd.org/kdd2019/accepted-papers/view/150-successful-machine-learning-models-6-lessons-learned-at-booking.com
Paperhttps://dl.acm.org/doi/pdf/10.1145/3292500.3330744
Successes and Challenges in Adopting Machine Learning at Scale at a Global Bankhttps://www.youtube.com/watch?v=QYQKG5OcwEI
Challenges in Deploying Machine Learning: a Survey of Case Studieshttps://arxiv.org/abs/2011.09926
Paperhttps://arxiv.org/pdf/2011.09926.pdf
Continuous Integration and Deployment for Machine Learning Online Serving and Modelshttps://eng.uber.com/continuous-integration-deployment-ml/
Tuning Model Performancehttps://eng.uber.com/tuning-model-performance/
Reengineering Facebook AI’s Deep Learning Platforms for Interoperabilityhttps://ai.facebook.com/blog/reengineering-facebook-ais-deep-learning-platforms-for-interoperability
The problem with AI developer tools for enterpriseshttps://towardsdatascience.com/the-problem-with-ai-developer-tools-for-enterprises-and-what-ikea-has-to-do-with-it-b26277841661
Maintaining Machine Learning Model Accuracy Through Monitoringhttps://doordash.engineering/2021/05/20/monitor-machine-learning-model-drift/
Building Scalable and Performant Marketing ML Systems at Wayfairhttps://www.aboutwayfair.com/careers/tech-blog/building-scalable-and-performant-marketing-ml-systems-at-wayfair
https://github.com/FerMatPy/applied-ml#team-structure
Engineers Shouldn’t Write ETL: A Guide to Building a High Functioning Data Science Departmenthttps://multithreaded.stitchfix.com/blog/2016/03/16/engineers-shouldnt-write-etl/
Beware the Data Science Pin Factory: The Power of the Full-Stack Data Science Generalisthttps://multithreaded.stitchfix.com/blog/2019/03/11/FullStackDS-Generalists/
Cultivating Algorithms: How We Grow Data Science at Stitch Fixhttps://cultivating-algos.stitchfix.com
Analytics at Netflix: Who We Are and What We Dohttps://netflixtechblog.com/analytics-at-netflix-who-we-are-and-what-we-do-7d9c08fe6965
https://github.com/FerMatPy/applied-ml#fails
160k+ High School Students Will Graduate Only If a Model Allows Them tohttp://positivelysemidefinite.com/2020/06/160k-students.html
When It Comes to Gorillas, Google Photos Remains Blindhttps://www.wired.com/story/when-it-comes-to-gorillas-google-photos-remains-blind/
An Algorithm That ‘Predicts’ Criminality Based on a Face Sparks a Furorhttps://www.wired.com/story/algorithm-predicts-criminality-based-face-sparks-furor/
It's Hard to Generate Neural Text From GPT-3 About Muslimshttps://twitter.com/abidlabs/status/1291165311329341440
A British AI Tool to Predict Violent Crime Is Too Flawed to Usehttps://www.wired.co.uk/article/police-violence-prediction-ndas
awful-aihttps://github.com/daviddao/awful-ai
ml-surveyshttps://github.com/eugeneyan/ml-surveys
Readme https://github.com/FerMatPy/applied-ml#readme-ov-file
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