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Curated papers, articles, and blogs ondata science & machine learning in production. ⚙️

Figuring out how to implement your ML project? Learn how other organizations did it:

• How the problem is framed 🔎(e.g., personalization as recsys vs. search vs. sequences)
• What machine learning techniques worked ✅ (and sometimes, what didn't ❌)
• Why it works, the science behind it with research, literature, and references 📂
• What real-world results were achieved (so you can better assess ROI ⏰💰📈)

P.S., Want a summary of ML advancements? 👉ml-surveys

P.P.S, Looking for guides and interviews on applying ML? 👉applyingML

Table of Contents

1. Data Quality
2. Data Engineering
3. Data Discovery
4. Feature Stores
5. Classification
6. Regression
7. Forecasting
8. Recommendation
9. Search & Ranking
10. Embeddings
11. Natural Language Processing
12. Sequence Modelling
13. Computer Vision
14. Reinforcement Learning
15. Anomaly Detection
16. Graph
17. Optimization
18. Information Extraction
19. Weak Supervision
20. Generation
21. Audio
22. Privacy-Preserving Machine Learning
23. Validation and A/B Testing
24. Model Management
25. Efficiency
26. Ethics
27. Infra
28. MLOps Platforms
29. Practices
30. Team Structure
31. Fails

1. Reliable and Scalable Data Ingestion at AirbnbAirbnb2016
2. Monitoring Data Quality at Scale with Statistical ModelingUber2017
3. Data Management Challenges in Production Machine Learning (Paper)Google2017
4. Automating Large-Scale Data Quality Verification (Paper)Amazon2018
5. Meet Hodor — Gojek’s Upstream Data Quality ToolGojek2019
6. Data Validation for Machine Learning (Paper)Google2019
7. An Approach to Data Quality for Netflix Personalization SystemsNetflix2020
8. Improving Accuracy By Certainty Estimation of Human Decisions, Labels, and Raters (Paper)Facebook2020

1. Zipline: Airbnb’s Machine Learning Data Management PlatformAirbnb2018
2. Sputnik: Airbnb’s Apache Spark Framework for Data EngineeringAirbnb2020
3. Unbundling Data Science Workflows with Metaflow and AWS Step FunctionsNetflix2020
4. How DoorDash is Scaling its Data Platform to Delight Customers and Meet Growing DemandDoorDash2020
5. Revolutionizing Money Movements at Scale with Strong Data ConsistencyUber2020
6. Zipline - A Declarative Feature Engineering FrameworkAirbnb2020
7. Automating Data Protection at Scale, Part 1 (Part 2)Airbnb2021
8. Real-time Data Infrastructure at UberUber2021
9. Introducing Fabricator: A Declarative Feature Engineering FrameworkDoorDash2022
10. Functions & DAGs: introducing Hamilton, a microframework for dataframe generationStitch Fix2021
11. Optimizing Pinterest’s Data Ingestion Stack: Findings and LearningsPinterest2022
12. Lessons Learned From Running Apache Airflow at ScaleShopify2022
13. Understanding Data Storage and Ingestion for Large-Scale Deep Recommendation Model TrainingMeta2022
14. Data Mesh — A Data Movement and Processing Platform @ NetflixNetflix2022
15. Building Scalable Real Time Event Processing with Kafka and FlinkDoorDash2022

1. Apache Atlas: Data Goverance and Metadata Framework for Hadoop (Code)Apache
2. Collect, Aggregate, and Visualize a Data Ecosystem's Metadata (Code)WeWork
3. Discovery and Consumption of Analytics Data at TwitterTwitter2016
4. Democratizing Data at AirbnbAirbnb2017
5. Databook: Turning Big Data into Knowledge with Metadata at UberUber2018
6. Metacat: Making Big Data Discoverable and Meaningful at Netflix (Code)Netflix2018
7. Amundsen — Lyft’s Data Discovery & Metadata EngineLyft2019
8. Open Sourcing Amundsen: A Data Discovery And Metadata Platform (Code)Lyft2019
9. DataHub: A Generalized Metadata Search & Discovery Tool (Code)LinkedIn2019
10. Amundsen: One Year LaterLyft2020
11. Using Amundsen to Support User Privacy via Metadata Collection at SquareSquare2020
12. Turning Metadata Into Insights with DatabookUber2020
13. DataHub: Popular Metadata Architectures ExplainedLinkedIn2020
14. How We Improved Data Discovery for Data Scientists at SpotifySpotify2020
15. How We’re Solving Data Discovery Challenges at ShopifyShopify2020
16. Nemo: Data discovery at FacebookFacebook2020
17. Exploring Data @ Netflix (Code)Netflix2021

1. Distributed Time Travel for Feature GenerationNetflix2016
2. Building the Activity Graph, Part 2 (Feature Storage Section)LinkedIn2017
3. Fact Store at Scale for Netflix RecommendationsNetflix2018
4. Zipline: Airbnb’s Machine Learning Data Management PlatformAirbnb2018
5. Feature Store: The missing data layer for Machine Learning pipelines?Hopsworks2018
6. Introducing Feast: An Open Source Feature Store for Machine Learning (Code)Gojek2019
7. Michelangelo Palette: A Feature Engineering Platform at UberUber2019
8. The Architecture That Powers Twitter's Feature StoreTwitter2019
9. Accelerating Machine Learning with the Feature Store ServiceCondé Nast2019
10. Feast: Bridging ML Models and DataGojek2020
11. Building a Scalable ML Feature Store with Redis, Binary Serialization, and CompressionDoorDash2020
12. Rapid Experimentation Through Standardization: Typed AI features for LinkedIn’s FeedLinkedIn2020
13. Building a Feature StoreMonzo Bank2020
14. Butterfree: A Spark-based Framework for Feature Store Building (Code)QuintoAndar2020
15. Building Riviera: A Declarative Real-Time Feature Engineering FrameworkDoorDash2021
16. Optimal Feature Discovery: Better, Leaner Machine Learning Models Through Information TheoryUber2021
17. ML Feature Serving Infrastructure at LyftLyft2021
18. Near real-time features for near real-time personalizationLinkedIn2022
19. Building the Model Behind DoorDash’s Expansive Merchant SelectionDoorDash2022
20. Open sourcing Feathr – LinkedIn’s feature store for productive machine learningLinkedIn2022
21. Evolution of ML Fact StoreNetflix2022
22. Developing scalable feature engineering DAGsMetaflow + Hamilton viaOuterbounds2022
23. Feature Store Design at ConstructorConstructor.io2023

1. Prediction of Advertiser Churn for Google AdWords (Paper)Google2010
2. High-Precision Phrase-Based Document Classification on a Modern Scale (Paper)LinkedIn2011
3. Chimera: Large-scale Classification using Machine Learning, Rules, and Crowdsourcing (Paper)Walmart2014
4. Large-scale Item Categorization in e-Commerce Using Multiple Recurrent Neural Networks (Paper)NAVER2016
5. Learning to Diagnose with LSTM Recurrent Neural Networks (Paper)Google2017
6. Discovering and Classifying In-app Message Intent at AirbnbAirbnb2019
7. Teaching Machines to Triage Firefox BugsMozilla2019
8. Categorizing Products at ScaleShopify2020
9. How We Built the Good First Issues FeatureGitHub2020
10. Testing Firefox More Efficiently with Machine LearningMozilla2020
11. Using ML to Subtype Patients Receiving Digital Mental Health Interventions (Paper)Microsoft2020
12. Scalable Data Classification for Security and Privacy (Paper)Facebook2020
13. Uncovering Online Delivery Menu Best Practices with Machine LearningDoorDash2020
14. Using a Human-in-the-Loop to Overcome the Cold Start Problem in Menu Item TaggingDoorDash2020
15. Deep Learning: Product Categorization and ShelvingWalmart2021
16. Large-scale Item Categorization for e-Commerce (Paper)DianPing,eBay2012
17. Semantic Label Representation with an Application on Multimodal Product CategorizationWalmart2022
18. Building Airbnb Categories with ML and Human-in-the-LoopAirbnb2022

1. Using Machine Learning to Predict Value of Homes On AirbnbAirbnb2017
2. Using Machine Learning to Predict the Value of Ad RequestsTwitter2020
3. Open-Sourcing Riskquant, a Library for Quantifying Risk (Code)Netflix2020
4. Solving for Unobserved Data in a Regression Model Using a Simple Data AdjustmentDoorDash2020

1. Engineering Extreme Event Forecasting at Uber with RNNUber2017
2. Forecasting at Uber: An IntroductionUber2018
3. Transforming Financial Forecasting with Data Science and Machine Learning at UberUber2018
4. Under the Hood of Gojek’s Automated Forecasting ToolGojek2019
5. BusTr: Predicting Bus Travel Times from Real-Time Traffic (Paper,Video)Google2020
6. Retraining Machine Learning Models in the Wake of COVID-19DoorDash2020
7. Automatic Forecasting using Prophet, Databricks, Delta Lake and MLflow (Paper,Code)Atlassian2020
8. Introducing Orbit, An Open Source Package for Time Series Inference and Forecasting (Paper,Video,Code)Uber2021
9. Managing Supply and Demand Balance Through Machine LearningDoorDash2021
10. Greykite: A flexible, intuitive, and fast forecasting libraryLinkedIn2021
11. The history of Amazon’s forecasting algorithmAmazon2021
12. DeepETA: How Uber Predicts Arrival Times Using Deep LearningUber2022
13. Forecasting Grubhub Order Volume At ScaleGrubhub2022
14. Causal Forecasting at Lyft (Part 1)Lyft2022

1. Amazon.com Recommendations: Item-to-Item Collaborative Filtering (Paper)Amazon2003
2. Netflix Recommendations: Beyond the 5 stars (Part 1 (Part 2)Netflix2012
3. How Music Recommendation Works — And Doesn’t WorkSpotify2012
4. Learning to Rank Recommendations with the k -Order Statistic Loss (Paper)Google2013
5. Recommending Music on Spotify with Deep LearningSpotify2014
6. Learning a Personalized HomepageNetflix2015
7. The Netflix Recommender System: Algorithms, Business Value, and Innovation (Paper)Netflix2015
8. Session-based Recommendations with Recurrent Neural Networks (Paper)Telefonica2016
9. Deep Neural Networks for YouTube RecommendationsYouTube2016
10. E-commerce in Your Inbox: Product Recommendations at Scale (Paper)Yahoo2016
11. To Be Continued: Helping you find shows to continue watching on NetflixNetflix2016
12. Personalized Recommendations in LinkedIn LearningLinkedIn2016
13. Personalized Channel Recommendations in SlackSlack2016
14. Recommending Complementary Products in E-Commerce Push Notifications (Paper)Alibaba2017
15. Artwork Personalization at NetflixNetflix2017
16. A Meta-Learning Perspective on Cold-Start Recommendations for Items (Paper)Twitter2017
17. Pixie: A System for Recommending 3+ Billion Items to 200+ Million Users in Real-Time (Paper)Pinterest2017
18. Powering Search & Recommendations at DoorDashDoorDash2017
19. How 20th Century Fox uses ML to predict a movie audience (Paper)20th Century Fox2018
20. Calibrated Recommendations (Paper)Netflix2018
21. Food Discovery with Uber Eats: Recommending for the MarketplaceUber2018
22. Explore, Exploit, and Explain: Personalizing Explainable Recommendations with Bandits (Paper)Spotify2018
23. Talent Search and Recommendation Systems at LinkedIn: Practical Challenges and Lessons Learned (Paper)LinkedIn2018
24. Behavior Sequence Transformer for E-commerce Recommendation in Alibaba (Paper)Alibaba2019
25. SDM: Sequential Deep Matching Model for Online Large-scale Recommender System (Paper)Alibaba2019
26. Multi-Interest Network with Dynamic Routing for Recommendation at Tmall (Paper)Alibaba2019
27. Personalized Recommendations for Experiences Using Deep LearningTripAdvisor2019
28. Powered by AI: Instagram’s Explore recommender systemFacebook2019
29. Marginal Posterior Sampling for Slate Bandits (Paper)Netflix2019
30. Food Discovery with Uber Eats: Using Graph Learning to Power RecommendationsUber2019
31. Music recommendation at SpotifySpotify2019
32. Using Machine Learning to Predict what File you Need Next (Part 1)Dropbox2019
33. Using Machine Learning to Predict what File you Need Next (Part 2)Dropbox2019
34. Learning to be Relevant: Evolution of a Course Recommendation System (PAPER NEEDED)LinkedIn2019
35. Temporal-Contextual Recommendation in Real-Time (Paper)Amazon2020
36. P-Companion: A Framework for Diversified Complementary Product Recommendation (Paper)Amazon2020
37. Deep Interest with Hierarchical Attention Network for Click-Through Rate Prediction (Paper)Alibaba2020
38. TPG-DNN: A Method for User Intent Prediction with Multi-task Learning (Paper)Alibaba2020
39. PURS: Personalized Unexpected Recommender System for Improving User Satisfaction (Paper)Alibaba2020
40. Controllable Multi-Interest Framework for Recommendation (Paper)Alibaba2020
41. MiNet: Mixed Interest Network for Cross-Domain Click-Through Rate Prediction (Paper)Alibaba2020
42. ATBRG: Adaptive Target-Behavior Relational Graph Network for Effective Recommendation (Paper)Alibaba2020
43. For Your Ears Only: Personalizing Spotify Home with Machine LearningSpotify2020
44. Reach for the Top: How Spotify Built Shortcuts in Just Six MonthsSpotify2020
45. Contextual and Sequential User Embeddings for Large-Scale Music Recommendation (Paper)Spotify2020
46. The Evolution of Kit: Automating Marketing Using Machine LearningShopify2020
47. A Closer Look at the AI Behind Course Recommendations on LinkedIn Learning (Part 1)LinkedIn2020
48. A Closer Look at the AI Behind Course Recommendations on LinkedIn Learning (Part 2)LinkedIn2020
49. Building a Heterogeneous Social Network Recommendation SystemLinkedIn2020
50. How TikTok recommends videos #ForYouByteDance2020
51. Zero-Shot Heterogeneous Transfer Learning from RecSys to Cold-Start Search Retrieval (Paper)Google2020
52. Improved Deep & Cross Network for Feature Cross Learning in Web-scale LTR Systems (Paper)Google2020
53. Mixed Negative Sampling for Learning Two-tower Neural Networks in Recommendations (Paper)Google2020
54. Future Data Helps Training: Modeling Future Contexts for Session-based Recommendation (Paper)Tencent2020
55. A Case Study of Session-based Recommendations in the Home-improvement Domain (Paper)Home Depot2020
56. Balancing Relevance and Discovery to Inspire Customers in the IKEA App (Paper)Ikea2020
57. How we use AutoML, Multi-task learning and Multi-tower models for Pinterest AdsPinterest2020
58. Multi-task Learning for Related Products Recommendations at PinterestPinterest2020
59. Improving the Quality of Recommended Pins with Lightweight RankingPinterest2020
60. Multi-task Learning and Calibration for Utility-based Home Feed RankingPinterest2020
61. Personalized Cuisine Filter Based on Customer Preference and Local PopularityDoorDash2020
62. How We Built a Matchmaking Algorithm to Cross-Sell ProductsGojek2020
63. Lessons Learned Addressing Dataset Bias in Model-Based Candidate Generation (Paper)Twitter2021
64. Self-supervised Learning for Large-scale Item Recommendations (Paper)Google2021
65. Deep Retrieval: End-to-End Learnable Structure Model for Large-Scale Recommendations (Paper)ByteDance2021
66. Using AI to Help Health Experts Address the COVID-19 PandemicFacebook2021
67. Advertiser Recommendation Systems at PinterestPinterest2021
68. On YouTube's Recommendation SystemYouTube2021
69. "Are you sure?": Preliminary Insights from Scaling Product Comparisons to Multiple ShopsCoveo2021
70. Mozrt, a Deep Learning Recommendation System Empowering Walmart Store AssociatesWalmart2021
71. Understanding Data Storage and Ingestion for Large-Scale Deep Recommendation Model Training (Paper)Meta2021
72. The Amazon Music conversational recommender is hitting the right notesAmazon2022
73. Personalized complementary product recommendation (Paper)Amazon2022
74. Building a Deep Learning Based Retrieval System for Personalized RecommendationseBay2022
75. How We Built: An Early-Stage Machine Learning Model for RecommendationsPeloton2022
76. Lessons Learned from Building out Context-Aware Recommender SystemsPeloton2022
77. Beyond Matrix Factorization: Using hybrid features for user-business recommendationsYelp2022
78. Improving job matching with machine-learned activity featuresLinkedIn2022
79. Understanding Data Storage and Ingestion for Large-Scale Deep Recommendation Model TrainingMeta2022
80. Blueprints for recommender system architectures: 10th anniversary editionXavier Amatriain2022
81. How Pinterest Leverages Realtime User Actions in Recommendation to Boost Homefeed Engagement VolumePinterest2022
82. RecSysOps: Best Practices for Operating a Large-Scale Recommender SystemNetflix2022
83. Recommend API: Unified end-to-end machine learning infrastructure to generate recommendationsSlack2022
84. Evolving DoorDash’s Substitution Recommendations AlgorithmDoorDash2022
85. Homepage Recommendation with Exploitation and ExplorationDoorDash2022
86. GPU-accelerated ML Inference at PinterestPinterest2022
87. Addressing Confounding Feature Issue for Causal Recommendation (Paper)Tencent2022

1. Amazon Search: The Joy of Ranking Products (Paper,Video,Code)Amazon2016
2. How Lazada Ranks Products to Improve Customer Experience and ConversionLazada2016
3. Ranking Relevance in Yahoo Search (Paper)Yahoo2016
4. Learning to Rank Personalized Search Results in Professional Networks (Paper)LinkedIn2016
5. Using Deep Learning at Scale in Twitter’s TimelinesTwitter2017
6. An Ensemble-based Approach to Click-Through Rate Prediction for Promoted Listings at Etsy (Paper)Etsy2017
7. Powering Search & Recommendations at DoorDashDoorDash2017
8. Applying Deep Learning To Airbnb Search (Paper)Airbnb2018
9. In-session Personalization for Talent Search (Paper)LinkedIn2018
10. Talent Search and Recommendation Systems at LinkedIn (Paper)LinkedIn2018
11. Food Discovery with Uber Eats: Building a Query Understanding EngineUber2018
12. Globally Optimized Mutual Influence Aware Ranking in E-Commerce Search (Paper)Alibaba2018
13. Reinforcement Learning to Rank in E-Commerce Search Engine (Paper)Alibaba2018
14. Semantic Product Search (Paper)Amazon2019
15. Machine Learning-Powered Search Ranking of Airbnb ExperiencesAirbnb2019
16. Entity Personalized Talent Search Models with Tree Interaction Features (Paper)LinkedIn2019
17. The AI Behind LinkedIn Recruiter Search and recommendation systemsLinkedIn2019
18. Learning Hiring Preferences: The AI Behind LinkedIn JobsLinkedIn2019
19. The Secret Sauce Behind Search PersonalisationGojek2019
20. Neural Code Search: ML-based Code Search Using Natural Language QueriesFacebook2019
21. Aggregating Search Results from Heterogeneous Sources via Reinforcement Learning (Paper)Alibaba2019
22. Cross-domain Attention Network with Wasserstein Regularizers for E-commerce SearchAlibaba2019
23. Understanding Searches Better Than Ever Before (Paper)Google2019
24. How We Used Semantic Search to Make Our Search 10x SmarterTokopedia2019
25. Query2vec: Search query expansion with query embeddingsGrubHub2019
26. MOBIUS: Towards the Next Generation of Query-Ad Matching in Baidu’s Sponsored SearchBaidu2019
27. Why Do People Buy Seemingly Irrelevant Items in Voice Product Search? (Paper)Amazon2020
28. Managing Diversity in Airbnb Search (Paper)Airbnb2020
29. Improving Deep Learning for Airbnb Search (Paper)Airbnb2020
30. Quality Matches Via Personalized AI for Hirer and Seeker PreferencesLinkedIn2020
31. Understanding Dwell Time to Improve LinkedIn Feed RankingLinkedIn2020
32. Ads Allocation in Feed via Constrained Optimization (Paper,Video)LinkedIn2020
33. Understanding Dwell Time to Improve LinkedIn Feed RankingLinkedIn2020
34. AI at Scale in BingMicrosoft2020
35. Query Understanding Engine in Traveloka Universal SearchTraveloka2020
36. Bayesian Product Ranking at WayfairWayfair2020
37. COLD: Towards the Next Generation of Pre-Ranking System (Paper)Alibaba2020
38. Shop The Look: Building a Large Scale Visual Shopping System at Pinterest (Paper,Video)Pinterest2020
39. Driving Shopping Upsells from Pinterest SearchPinterest2020
40. GDMix: A Deep Ranking Personalization Framework (Code)LinkedIn2020
41. Bringing Personalized Search to EtsyEtsy2020
42. Building a Better Search Engine for Semantic ScholarAllen Institute for AI2020
43. Query Understanding for Natural Language Enterprise Search (Paper)Salesforce2020
44. Things Not Strings: Understanding Search Intent with Better RecallDoorDash2020
45. Query Understanding for Surfacing Under-served Music Content (Paper)Spotify2020
46. Embedding-based Retrieval in Facebook Search (Paper)Facebook2020
47. Towards Personalized and Semantic Retrieval for E-commerce Search via Embedding Learning (Paper)JD2020
48. QUEEN: Neural query rewriting in e-commerce (Paper)Amazon2021
49. Using Learning-to-rank to Precisely Locate Where to Deliver Packages (Paper)Amazon2021
50. Seasonal relevance in e-commerce search (Paper)Amazon2021
51. Graph Intention Network for Click-through Rate Prediction in Sponsored Search (Paper)Alibaba2021
52. How We Built A Context-Specific Bidding System for Etsy AdsEtsy2021
53. Pre-trained Language Model based Ranking in Baidu Search (Paper)Baidu2021
54. Stitching together spaces for query-based recommendationsStitch Fix2021
55. Deep Natural Language Processing for LinkedIn Search Systems (Paper)LinkedIn2021
56. Siamese BERT-based Model for Web Search Relevance Ranking (Paper,Code)Seznam2021
57. SearchSage: Learning Search Query Representations at PinterestPinterest2021
58. Query2Prod2Vec: Grounded Word Embeddings for eCommerceCoveo2021
59. 3 Changes to Expand DoorDash’s Product Search Beyond DeliveryDoorDash2022
60. Learning To Rank DiverselyAirbnb2022
61. How to Optimise Rankings with Cascade BanditsExpedia2022
62. A Guide to Google Search Ranking SystemsGoogle2022
63. Deep Learning for Search Ranking at EtsyEtsy2022
64. Search at CalmCalm2022

1. Vector Representation Of Items, Customer And Cart To Build A Recommendation System (Paper)Sears2017
2. Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba (Paper)Alibaba2018
3. Embeddings@TwitterTwitter2018
4. Listing Embeddings in Search Ranking (Paper)Airbnb2018
5. Understanding Latent StyleStitch Fix2018
6. Towards Deep and Representation Learning for Talent Search at LinkedIn (Paper)LinkedIn2018
7. Personalized Store Feed with Vector EmbeddingsDoorDash2018
8. Should we Embed? A Study on Performance of Embeddings for Real-Time Recommendations(Paper)Moshbit2019
9. Machine Learning for a Better Developer ExperienceNetflix2020
10. Announcing ScaNN: Efficient Vector Similarity Search (Paper,Code)Google2020
11. BERT Goes Shopping: Comparing Distributional Models for Product RepresentationsCoveo2021
12. The Embeddings That Came in From the Cold: Improving Vectors for New and Rare Products with Content-Based InferenceCoveo2022
13. Embedding-based Retrieval at ScribdScribd2021
14. Multi-objective Hyper-parameter Optimization of Behavioral Song Embeddings (Paper)Apple2022
15. Embeddings at Spotify's Scale - How Hard Could It Be?Spotify2023

1. Abusive Language Detection in Online User Content (Paper)Yahoo2016
2. Smart Reply: Automated Response Suggestion for Email (Paper)Google2016
3. Building Smart Replies for Member MessagesLinkedIn2017
4. How Natural Language Processing Helps LinkedIn Members Get Support EasilyLinkedIn2019
5. Gmail Smart Compose: Real-Time Assisted Writing (Paper)Google2019
6. Goal-Oriented End-to-End Conversational Models with Profile Features in a Real-World Setting (Paper)Amazon2019
7. Give Me Jeans not Shoes: How BERT Helps Us Deliver What Clients WantStitch Fix2019
8. DeText: A deep NLP Framework for Intelligent Text Understanding (Code)LinkedIn2020
9. SmartReply for YouTube CreatorsGoogle2020
10. Using Neural Networks to Find Answers in Tables (Paper)Google2020
11. A Scalable Approach to Reducing Gender Bias in Google TranslateGoogle2020
12. Assistive AI Makes Replying EasierMicrosoft2020
13. AI Advances to Better Detect Hate SpeechFacebook2020
14. A State-of-the-Art Open Source Chatbot (Paper)Facebook2020
15. A Highly Efficient, Real-Time Text-to-Speech System Deployed on CPUsFacebook2020
16. Deep Learning to Translate Between Programming Languages (Paper,Code)Facebook2020
17. Deploying Lifelong Open-Domain Dialogue Learning (Paper)Facebook2020
18. Introducing Dynabench: Rethinking the way we benchmark AIFacebook2020
19. How Gojek Uses NLP to Name Pickup Locations at ScaleGojek2020
20. The State-of-the-art Open-Domain Chatbot in Chinese and English (Paper)Baidu2020
21. PEGASUS: A State-of-the-Art Model for Abstractive Text Summarization (Paper,Code)Google2020
22. Photon: A Robust Cross-Domain Text-to-SQL System (Paper) (Demo)Salesforce2020
23. GeDi: A Powerful New Method for Controlling Language Models (Paper,Code)Salesforce2020
24. Applying Topic Modeling to Improve Call Center OperationsRICOH2020
25. WIDeText: A Multimodal Deep Learning FrameworkAirbnb2020
26. Dynaboard: Moving Beyond Accuracy to Holistic Model Evaluation in NLP (Code)Facebook2021
27. How we reduced our text similarity runtime by 99.96%Microsoft2021
28. Textless NLP: Generating expressive speech from raw audio(Part 1)(Part 2)(Part 3)(Code and Pretrained Models)Facebook2021
29. Grammar Correction as You Type, on Pixel 6Google2021
30. Auto-generated Summaries in Google DocsGoogle2022
31. ML-Enhanced Code Completion Improves Developer ProductivityGoogle2022
32. Words All the Way Down — Conversational Sentiment AnalysisPayPal2022

1. Doctor AI: Predicting Clinical Events via Recurrent Neural Networks (Paper)Sutter Health2015
2. Deep Learning for Understanding Consumer Histories (Paper)Zalando2016
3. Using Recurrent Neural Network Models for Early Detection of Heart Failure Onset (Paper)Sutter Health2016
4. Continual Prediction of Notification Attendance with Classical and Deep Networks (Paper)Telefonica2017
5. Deep Learning for Electronic Health Records (Paper)Google2018
6. Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction (Paper)Alibaba2019
7. Search-based User Interest Modeling with Sequential Behavior Data for CTR Prediction (Paper)Alibaba2020
8. How Duolingo uses AI in every part of its appDuolingo2020
9. Leveraging Online Social Interactions For Enhancing Integrity at Facebook (Paper,Video)Facebook2020
10. Using deep learning to detect abusive sequences of member activity (Video)LinkedIn2021

1. Creating a Modern OCR Pipeline Using Computer Vision and Deep LearningDropbox2017
2. Categorizing Listing Photos at AirbnbAirbnb2018
3. Amenity Detection and Beyond — New Frontiers of Computer Vision at AirbnbAirbnb2019
4. How we Improved Computer Vision Metrics by More Than 5% Only by Cleaning Labelling ErrorsDeepomatic
5. Making machines recognize and transcribe conversations in meetings using audio and videoMicrosoft2019
6. Powered by AI: Advancing product understanding and building new shopping experiencesFacebook2020
7. A Neural Weather Model for Eight-Hour Precipitation Forecasting (Paper)Google2020
8. Machine Learning-based Damage Assessment for Disaster Relief (Paper)Google2020
9. RepNet: Counting Repetitions in Videos (Paper)Google2020
10. Converting Text to Images for Product Discovery (Paper)Amazon2020
11. How Disney Uses PyTorch for Animated Character RecognitionDisney2020
12. Image Captioning as an Assistive Technology (Video)IBM2020
13. AI for AG: Production machine learning for agricultureBlue River2020
14. AI for Full-Self Driving at TeslaTesla2020
15. On-device Supermarket Product RecognitionGoogle2020
16. Using Machine Learning to Detect Deficient Coverage in Colonoscopy Screenings (Paper)Google2020
17. Shop The Look: Building a Large Scale Visual Shopping System at Pinterest (Paper,Video)Pinterest2020
18. Developing Real-Time, Automatic Sign Language Detection for Video Conferencing (Paper)Google2020
19. Vision-based Price Suggestion for Online Second-hand Items (Paper)Alibaba2020
20. New AI Research to Help Predict COVID-19 Resource Needs From X-rays (Paper,Model)Facebook2021
21. An Efficient Training Approach for Very Large Scale Face Recognition (Paper)Alibaba2021
22. Identifying Document Types at ScribdScribd2021
23. Semi-Supervised Visual Representation Learning for Fashion Compatibility (Paper)Walmart2021
24. Recognizing People in Photos Through Private On-Device Machine LearningApple2021
25. DeepFusion: Lidar-Camera Deep Fusion for Multi-Modal 3D Object DetectionGoogle2022
26. Contrastive language and vision learning of general fashion concepts (Paper)Coveo2022
27. Leveraging Computer Vision for Search RankingBazaarVoice2023

1. Deep Reinforcement Learning for Sponsored Search Real-time Bidding (Paper)Alibaba2018
2. Budget Constrained Bidding by Model-free Reinforcement Learning in Display Advertising (Paper)Alibaba2018
3. Reinforcement Learning for On-Demand LogisticsDoorDash2018
4. Reinforcement Learning to Rank in E-Commerce Search Engine (Paper)Alibaba2018
5. Dynamic Pricing on E-commerce Platform with Deep Reinforcement Learning (Paper)Alibaba2019
6. Productionizing Deep Reinforcement Learning with Spark and MLflowZynga2020
7. Deep Reinforcement Learning in Production Part1Part 2Zynga2020
8. Building AI Trading SystemsDenny Britz2020
9. Shifting Consumption towards Diverse content via Reinforcement Learning (Paper)Spotify2022
10. Bandits for Online Calibration: An Application to Content Moderation on Social Media PlatformsMeta2022
11. How to Optimise Rankings with Cascade BanditsExpedia2022
12. Selecting the Best Image for Each Merchant Using Exploration and Machine LearningDoorDash2023

1. Detecting Performance Anomalies in External Firmware DeploymentsNetflix2019
2. Detecting and Preventing Abuse on LinkedIn using Isolation Forests (Code)LinkedIn2019
3. Deep Anomaly Detection with Spark and Tensorflow(Hopsworks Video)Swedbank,Hopsworks2019
4. Preventing Abuse Using Unsupervised LearningLinkedIn2020
5. The Technology Behind Fighting Harassment on LinkedInLinkedIn2020
6. Uncovering Insurance Fraud Conspiracy with Network Learning (Paper)Ant Financial2020
7. How Does Spam Protection Work on Stack Exchange?Stack Exchange2020
8. Auto Content Moderation in C2C e-CommerceMercari2020
9. Blocking Slack Invite Spam With Machine LearningSlack2020
10. Cloudflare Bot Management: Machine Learning and MoreCloudflare2020
11. Anomalies in Oil Temperature Variations in a Tunnel Boring MachineSENER2020
12. Using Anomaly Detection to Monitor Low-Risk Bank CustomersRabobank2020
13. Fighting fraud with Triplet LossOLX Group2020
14. Facebook is Now Using AI to Sort Content for Quicker Moderation (Alternative)Facebook2020
15. How AI is getting better at detecting hate speechPart 1,Part 2,Part 3,Part 4Facebook2020
16. Using deep learning to detect abusive sequences of member activity (Video)LinkedIn2021
17. Project RADAR: Intelligent Early Fraud Detection System with Humans in the LoopUber2022
18. Graph for Fraud DetectionGrab2022
19. Bandits for Online Calibration: An Application to Content Moderation on Social Media PlatformsMeta2022
20. Evolving our machine learning to stop mobile botsCloudflare2022
21. Improving the accuracy of our machine learning WAF using data augmentation and samplingCloudflare2022
22. Machine Learning for Fraud Detection in Streaming ServicesNetflix2022
23. Pricing at LyftLyft2022

1. Building The LinkedIn Knowledge GraphLinkedIn2016
2. Scaling Knowledge Access and Retrieval at AirbnbAirbnb2018
3. Graph Convolutional Neural Networks for Web-Scale Recommender Systems (Paper)Pinterest2018
4. Food Discovery with Uber Eats: Using Graph Learning to Power RecommendationsUber2019
5. AliGraph: A Comprehensive Graph Neural Network Platform (Paper)Alibaba2019
6. Contextualizing Airbnb by Building Knowledge GraphAirbnb2019
7. Retail Graph — Walmart’s Product Knowledge GraphWalmart2020
8. Traffic Prediction with Advanced Graph Neural NetworksDeepMind2020
9. SimClusters: Community-Based Representations for Recommendations (Paper,Video)Twitter2020
10. Metapaths guided Neighbors aggregated Network for Heterogeneous Graph Reasoning (Paper)Alibaba2021
11. Graph Intention Network for Click-through Rate Prediction in Sponsored Search (Paper)Alibaba2021
12. JEL: Applying End-to-End Neural Entity Linking in JPMorgan Chase (Paper)JPMorgan Chase2021
13. How AWS uses graph neural networks to meet customer needsAmazon2022
14. Graph for Fraud DetectionGrab2022

1. Matchmaking in Lyft Line (Part 1)(Part 2)(Part 3)Lyft2016
2. The Data and Science behind GrabShare Carpooling(Part 1) (PAPER NEEDED)Grab2017
3. How Trip Inferences and Machine Learning Optimize Delivery Times on Uber EatsUber2018
4. Next-Generation Optimization for Dasher Dispatch at DoorDashDoorDash2020
5. Optimization of Passengers Waiting Time in Elevators Using Machine LearningThyssen Krupp AG2020
6. Think Out of The Package: Recommending Package Types for E-commerce Shipments (Paper)Amazon2020
7. Optimizing DoorDash’s Marketing Spend with Machine LearningDoorDash2020
8. Using learning-to-rank to precisely locate where to deliver packages (Paper)Amazon2021

1. Unsupervised Extraction of Attributes and Their Values from Product Description (Paper)Rakuten2013
2. Using Machine Learning to Index Text from Billions of ImagesDropbox2018
3. Extracting Structured Data from Templatic Documents (Paper)Google2020
4. AutoKnow: self-driving knowledge collection for products of thousands of types (Paper,Video)Amazon2020
5. One-shot Text Labeling using Attention and Belief Propagation for Information Extraction (Paper)Alibaba2020
6. Information Extraction from Receipts with Graph Convolutional NetworksNanonets2021

1. Snorkel DryBell: A Case Study in Deploying Weak Supervision at Industrial Scale (Paper)Google2019
2. Osprey: Weak Supervision of Imbalanced Extraction Problems without Code (Paper)Intel2019
3. Overton: A Data System for Monitoring and Improving Machine-Learned Products (Paper)Apple2019
4. Bootstrapping Conversational Agents with Weak Supervision (Paper)IBM2019

1. Better Language Models and Their Implications (Paper)OpenAI2019
2. Image GPT (Paper,Code)OpenAI2019
3. Language Models are Few-Shot Learners (Paper) (GPT-3 Blog post)OpenAI2020
4. Deep Learned Super Resolution for Feature Film Production (Paper)Pixar2020
5. Unit Test Case Generation with TransformersMicrosoft2021

1. Improving On-Device Speech Recognition with VoiceFilter-Lite (Paper)Google2020
2. The Machine Learning Behind Hum to SearchGoogle2020

1. Federated Learning: Collaborative Machine Learning without Centralized Training Data (Paper)Google2017
2. Federated Learning with Formal Differential Privacy Guarantees (Paper)Google2022
3. MPC-based machine learning: Achieving end-to-end privacy-preserving machine learning (Paper)Facebook2022

1. Overlapping Experiment Infrastructure: More, Better, Faster Experimentation (Paper)Google2010
2. The Reusable Holdout: Preserving Validity in Adaptive Data Analysis (Paper)Google2015
3. Twitter Experimentation: Technical OverviewTwitter2015
4. It’s All A/Bout Testing: The Netflix Experimentation PlatformNetflix2016
5. Building Pinterest’s A/B Testing PlatformPinterest2016
6. Experimenting to Solve CrammingTwitter2017
7. Building an Intelligent Experimentation Platform with Uber EngineeringUber2017
8. Scaling Airbnb’s Experimentation PlatformAirbnb2017
9. Meet Wasabi, an Open Source A/B Testing Platform (Code)Intuit2017
10. Analyzing Experiment Outcomes: Beyond Average Treatment EffectsUber2018
11. Under the Hood of Uber’s Experimentation PlatformUber2018
12. Constrained Bayesian Optimization with Noisy Experiments (Paper)Facebook2018
13. Reliable and Scalable Feature Toggles and A/B Testing SDK at GrabGrab2018
14. Modeling Conversion Rates and Saving Millions Using Kaplan-Meier and Gamma Distributions (Code)Better2019
15. Detecting Interference: An A/B Test of A/B TestsLinkedIn2019
16. Announcing a New Framework for Designing Optimal Experiments with Pyro (Paper) (Paper)Uber2020
17. Enabling 10x More Experiments with Traveloka Experiment PlatformTraveloka2020
18. Large Scale Experimentation at Stitch Fix (Paper)Stitch Fix2020
19. Multi-Armed Bandits and the Stitch Fix Experimentation PlatformStitch Fix2020
20. Experimentation with Resource ConstraintsStitch Fix2020
21. Computational Causal Inference at Netflix (Paper)Netflix2020
22. Key Challenges with Quasi Experiments at NetflixNetflix2020
23. Making the LinkedIn experimentation engine 20x fasterLinkedIn2020
24. Our Evolution Towards T-REX: The Prehistory of Experimentation Infrastructure at LinkedInLinkedIn2020
25. How to Use Quasi-experiments and Counterfactuals to Build Great ProductsShopify2020
26. Improving Experimental Power through Control Using Predictions as CovariateDoorDash2020
27. Supporting Rapid Product Iteration with an Experimentation Analysis PlatformDoorDash2020
28. Improving Online Experiment Capacity by 4X with Parallelization and Increased SensitivityDoorDash2020
29. Leveraging Causal Modeling to Get More Value from Flat Experiment ResultsDoorDash2020
30. Iterating Real-time Assignment Algorithms Through ExperimentationDoorDash2020
31. Spotify’s New Experimentation Platform (Part 1)(Part 2)Spotify2020
32. Interpreting A/B Test Results: False Positives and Statistical SignificanceNetflix2021
33. Interpreting A/B Test Results: False Negatives and PowerNetflix2021
34. Running Experiments with Google Adwords for Campaign OptimizationDoorDash2021
35. The 4 Principles DoorDash Used to Increase Its Logistics Experiment Capacity by 1000%DoorDash2021
36. Experimentation Platform at Zalando: Part 1 - EvolutionZalando2021
37. Designing Experimentation GuardrailsAirbnb2021
38. How Airbnb Measures Future Value to Standardize TradeoffsAirbnb2021
39. Network Experimentation at Scale(Paper]Facebook2021
40. Universal Holdout Groups at Disney StreamingDisney2021
41. Experimentation is a major focus of Data Science across NetflixNetflix2022
42. Search Journey Towards Better Experimentation PracticesSpotify2022
43. Artificial Counterfactual Estimation: Machine Learning-Based Causal Inference at AirbnbAirbnb2022
44. Beyond A/B Test : Speeding up Airbnb Search Ranking Experimentation through InterleavingAirbnb2022
45. Challenges in ExperimentationLyft2022
46. Overtracking and Trigger Analysis: Reducing sample sizes while INCREASING sensitivityBooking2022
47. Meet Dash-AB — The Statistics Engine of Experimentation at DoorDashDoorDash2022
48. Comparing quantiles at scale in online A/B-testingSpotify2022
49. Accelerating our A/B experiments with machine learningDropbox2023
50. Supercharging A/B Testing at UberUber

1. Operationalizing Machine Learning—Managing Provenance from Raw Data to PredictionsComcast2018
2. Overton: A Data System for Monitoring and Improving Machine-Learned Products (Paper)Apple2019
3. Runway - Model Lifecycle Management at NetflixNetflix2020
4. Managing ML Models @ Scale - Intuit’s ML PlatformIntuit2020
5. ML Model Monitoring - 9 Tips From the TrenchesNubank2021
6. Dealing with Train-serve Skew in Real-time ML Models: A Short GuideNubank2023

1. GrokNet: Unified Computer Vision Model Trunk and Embeddings For Commerce (Paper)Facebook2020
2. How We Scaled Bert To Serve 1+ Billion Daily Requests on CPUsRoblox2020
3. Permute, Quantize, and Fine-tune: Efficient Compression of Neural Networks (Paper)Uber2021
4. GPU-accelerated ML Inference at PinterestPinterest2022

1. Building Inclusive Products Through A/B Testing (Paper)LinkedIn2020
2. LiFT: A Scalable Framework for Measuring Fairness in ML Applications (Paper)LinkedIn2020
3. Introducing Twitter’s first algorithmic bias bounty challengeTwitter2021
4. Examining algorithmic amplification of political content on TwitterTwitter2021
5. A closer look at how LinkedIn integrates fairness into its AI productsLinkedIn2022

1. Reengineering Facebook AI’s Deep Learning Platforms for InteroperabilityFacebook2020
2. Elastic Distributed Training with XGBoost on RayUber2021

1. Meet Michelangelo: Uber’s Machine Learning PlatformUber2017
2. Operationalizing Machine Learning—Managing Provenance from Raw Data to PredictionsComcast2018
3. Big Data Machine Learning Platform at PinterestPinterest2019
4. Core Modeling at InstagramInstagram2019
5. Open-Sourcing Metaflow - a Human-Centric Framework for Data ScienceNetflix2019
6. Managing ML Models @ Scale - Intuit’s ML PlatformIntuit2020
7. Real-time Machine Learning Inference Platform at ZomatoZomato2020
8. Introducing Flyte: Cloud Native Machine Learning and Data Processing PlatformLyft2020
9. Building Flexible Ensemble ML Models with a Computational GraphDoorDash2021
10. LyftLearn: ML Model Training Infrastructure built on KubernetesLyft2021
11. "You Don't Need a Bigger Boat": A Full Data Pipeline Built with Open-Source Tools (Paper)Coveo2021
12. MLOps at GreenSteam: Shipping Machine LearningGreenSteam2021
13. Evolving Reddit’s ML Model Deployment and Serving ArchitectureReddit2021
14. Redesigning Etsy’s Machine Learning PlatformEtsy2021
15. Understanding Data Storage and Ingestion for Large-Scale Deep Recommendation Model Training (Paper)Meta2021
16. Building a Platform for Serving Recommendations at EtsyEtsy2022
17. Intelligent Automation Platform: Empowering Conversational AI and Beyond at AirbnbAirbnb2022
18. DARWIN: Data Science and Artificial Intelligence Workbench at LinkedInLinkedIn2022
19. The Magic of Merlin: Shopify's New Machine Learning PlatformShopify2022
20. Zalando's Machine Learning PlatformZalando2022
21. Inside Meta's AI optimization platform for engineers across the company (Paper)Meta2022
22. Monzo’s machine learning stackMonzo2022
23. Evolution of ML Fact StoreNetflix2022
24. Using MLOps to Build a Real-time End-to-End Machine Learning PipelineBinance2022
25. Serving Machine Learning Models Efficiently at Scale at ZillowZillow2022
26. Didact AI: The anatomy of an ML-powered stock picking engineDidact AI2022
27. Deployment for Free - A Machine Learning Platform for Stitch Fix's Data ScientistsStitch Fix2022
28. Machine Learning Operations (MLOps): Overview, Definition, and Architecture (Paper)IBM2022

1. Practical Recommendations for Gradient-Based Training of Deep Architectures (Paper)Yoshua Bengio2012
2. Machine Learning: The High Interest Credit Card of Technical Debt (Paper) (Paper)Google2014
3. Rules of Machine Learning: Best Practices for ML EngineeringGoogle2018
4. On Challenges in Machine Learning Model ManagementAmazon2018
5. Machine Learning in Production: The Booking.com ApproachBooking2019
6. 150 Successful Machine Learning Models: 6 Lessons Learned at Booking.com (Paper)Booking2019
7. Successes and Challenges in Adopting Machine Learning at Scale at a Global BankRabobank2019
8. Challenges in Deploying Machine Learning: a Survey of Case Studies (Paper)Cambridge2020
9. Reengineering Facebook AI’s Deep Learning Platforms for InteroperabilityFacebook2020
10. The problem with AI developer tools for enterprisesDatabricks2020
11. Continuous Integration and Deployment for Machine Learning Online Serving and ModelsUber2021
12. Tuning Model PerformanceUber2021
13. Maintaining Machine Learning Model Accuracy Through MonitoringDoorDash2021
14. Building Scalable and Performant Marketing ML Systems at WayfairWayfair2021
15. Our approach to building transparent and explainable AI systemsLinkedIn2021
16. 5 Steps for Building Machine Learning Models for BusinessShopify2021
17. Data Is An Art, Not Just A Science—And Storytelling Is The KeyShopify2022
18. Best Practices for Real-time Machine Learning: AlertingNubank2022
19. Automatic Retraining for Machine Learning Models: Tips and Lessons LearnedNubank2022
20. RecSysOps: Best Practices for Operating a Large-Scale Recommender SystemNetflix2022
21. ML Education at Uber: Frameworks Inspired by Engineering PrinciplesUber2022
22. Building and Maintaining Internal Tools for DS/ML teams: Lessons LearnedNubank2024

1. What is the most effective way to structure a data science team?Udemy2017
2. Engineers Shouldn’t Write ETL: A Guide to Building a High Functioning Data Science DepartmentStitch Fix2016
3. Building The Analytics Team At WishWish2018
4. Beware the Data Science Pin Factory: The Power of the Full-Stack Data Science GeneralistStitch Fix2019
5. Cultivating Algorithms: How We Grow Data Science at Stitch FixStitch Fix
6. Analytics at Netflix: Who We Are and What We DoNetflix2020
7. Building a Data Team at a Mid-stage Startup: A Short StoryErikbern2021
8. A Behind-the-Scenes Look at How Postman’s Data Team WorksPostman2021
9. Data Scientist x Machine Learning Engineer Roles: How are they different? How are they alike?Nubank2022

1. When It Comes to Gorillas, Google Photos Remains BlindGoogle2018
2. 160k+ High School Students Will Graduate Only If a Model Allows Them toInternational Baccalaureate2020
3. An Algorithm That ‘Predicts’ Criminality Based on a Face Sparks a FurorHarrisburg University2020
4. It's Hard to Generate Neural Text From GPT-3 About MuslimsOpenAI2020
5. A British AI Tool to Predict Violent Crime Is Too Flawed to UseUnited Kingdom2020
6. More inawful-ai
7. AI Incident DatabasePartnership on AI2022

P.S., Want a summary of ML advancements? Get up to speed with survey papers 👉ml-surveys