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📚 Papers & articles of companies sharing their work on applied data science & machine learning.

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applied-ml

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

contributions welcome Summaries HitCount

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

Table of Contents

  1. Data Quality
  2. Data Engineering
  3. Data Discovery
  4. Classification
  5. Regression
  6. Recommendation
  7. Search & Ranking
  8. Embeddings
  9. Natural Language Processing
  10. Sequence Modelling
  11. Forecasting
  12. Computer Vision
  13. Reinforcement Learning
  14. Anomaly Detection
  15. Graph
  16. Optimization
  17. Information Extraction
  18. Weak Supervision
  19. Generation
  20. Validation and A/B Testing
  21. Practices
  22. Fails

Data Quality

  1. Monitoring Data Quality at Scale with Statistical Modeling Uber
  2. An Approach to Data Quality for Netflix Personalization Systems Netflix
  3. Automating Large-Scale Data Quality Verification (Paper)Amazon
  4. Meet Hodor — Gojek’s Upstream Data Quality Tool Gojek
  5. Reliable and Scalable Data Ingestion at Airbnb Airbnb
  6. Data Management Challenges in Production Machine Learning (Paper) Google

Data Engineering

  1. Zipline: Airbnb’s Machine Learning Data Management Platform Airbnb
  2. Sputnik: Airbnb’s Apache Spark Framework for Data Engineering Airbnb
  3. Introducing Feast: an open source feature store for machine learning (Code)Gojek
  4. Feast: Bridging ML Models and Data Gojek

Data Discovery

  1. Amundsen — Lyft’s Data Discovery & Metadata Engine Lyft
  2. Open Sourcing Amundsen: A Data Discovery And Metadata Platform (Code) Lyft
  3. Using Amundsen to Support User Privacy via Metadata Collection at Square Square
  4. Democratizing Data at Airbnb Airbnb
  5. Databook: Turning Big Data into Knowledge with Metadata at Uber Uber
  6. Metacat: Making Big Data Discoverable and Meaningful at Netflix Netflix
  7. DataHub: A Generalized Metadata Search & Discovery Tool LinkedIn
  8. How We Improved Data Discovery for Data Scientists at Spotify Spotify
  9. How We’re Solving Data Discovery Challenges at Shopify Shopify

Classification

  1. High-Precision Phrase-Based Document Classification on a Modern Scale (Paper) LinkedIn
  2. Chimera: Large-Scale Classification using Machine Learning, Rules, and Crowdsourcing (Paper) WalmartLabs
  3. Large-scale Item Categorization for e-Commerce (Paper) DianPing, eBay
  4. Large-Scale Item Categorization in e-Commerce Using Multiple Recurrent Neural Networks (Paper) NAVER
  5. Categorizing Products at Scale Shopify
  6. Learning to Diagnose with LSTM Recurrent Neural Networks (Paper) Google
  7. Discovering and Classifying In-app Message Intent at Airbnb Airbnb
  8. How We Built the Good First Issues Feature GitHub
  9. Teaching Machines to Triage Firefox Bugs Mozilla
  10. Testing Firefox More Efficiently with Machine Learning Mozilla
  11. Using ML to Subtype Patients Receiving Digital Mental Health Interventions (Paper) Microsoft
  12. Prediction of Advertiser Churn for Google AdWords (Paper) Google
  13. Scalable Data Classification for Security and Privacy (Paper) Facebook

Regression

  1. Using Machine Learning to Predict Value of Homes On Airbnb Airbnb
  2. Using Machine Learning to Predict the Value of Ad Requests Twitter
  3. Open-Sourcing Riskquant, a Library for Quantifying Risk (Code) NetFlix

Recommendation

  1. Amazon.com Recommendations: Item-to-Item Collaborative Filtering (Paper) Amazon
  2. Recommending Complementary Products in E-Commerce Push Notifications (Paper) Alibaba
  3. Behavior Sequence Transformer for E-commerce Recommendation in Alibaba (Paper) Alibaba
  4. TPG-DNN: A Method for User Intent Prediction with Multi-task Learning (Paper) Alibaba
  5. Session-based Recommendations with Recurrent Neural Networks (Paper) Telefonica
  6. How 20th Century Fox uses ML to predict a movie audience (Paper) 20th Century Fox
  7. Deep Neural Networks for YouTube Recommendations YouTube
  8. Personalized Recommendations for Experiences Using Deep Learning TripAdvisor
  9. E-commerce in Your Inbox: Product Recommendations at Scale Yahoo
  10. Product Recommendations at Scale (Paper) Yahoo
  11. Powered by AI: Instagram’s Explore recommender system Facebook
  12. Netflix Recommendations: Beyond the 5 stars (Part 1 (Part 2) Netflix
  13. Learning a Personalized Homepage Netflix
  14. Artwork Personalization at Netflix Netflix
  15. To Be Continued: Helping you find shows to continue watching on Netflix Netflix
  16. Calibrated Recommendations (Paper) Netflix
  17. Food Discovery with Uber Eats: Recommending for the Marketplace Uber
  18. Food Discovery with Uber Eats: Using Graph Learning to Power Recommendations Uber
  19. How Music Recommendation Works — And Doesn’t Work Spotify
  20. Music recommendation at Spotify Spotify
  21. Recommending Music on Spotify with Deep Learning Spotify
  22. For Your Ears Only: Personalizing Spotify Home with Machine Learning Spotify
  23. Reach for the Top: How Spotify Built Shortcuts in Just Six Months Spotify
  24. Explore, Exploit, and Explain: Personalizing Explainable Recommendations with Bandits (Paper) Spotify
  25. The Evolution of Kit: Automating Marketing Using Machine Learning Shopify
  26. Using Machine Learning to Predict what File you Need Next (Part 1) Dropbox
  27. Using Machine Learning to Predict what File you Need Next (Part 2) Dropbox
  28. Personalized Recommendations in LinkedIn Learning LinkedIn
  29. A Closer Look at the AI Behind Course Recommendations on LinkedIn Learning (Part 1) LinkedIn
  30. A Closer Look at the AI Behind Course Recommendations on LinkedIn Learning (Part 2) LinkedIn
  31. Learning to be Relevant: Evolution of a Course Recommendation System (PAPER NEEDED)LinkedIn
  32. How TikTok recommends videos #ForYou ByteDance
  33. A Meta-Learning Perspective on Cold-Start Recommendations for Items (Paper) Twitter

Search & Ranking

  1. Amazon Search: The Joy of Ranking Products (Paper) Amazon
  2. Why Do People Buy Seemingly Irrelevant Items in Voice Product Search? (Paper) Amazon
  3. How Lazada Ranks Products to Improve Customer Experience and Conversion Lazada
  4. Using Deep Learning at Scale in Twitter’s Timelines Twitter
  5. Machine Learning-Powered Search Ranking of Airbnb Experiences Airbnb
  6. Applying Deep Learning To Airbnb Search (Paper) Airbnb
  7. Managing Diversity in Airbnb Search (Paper) Airbnb
  8. Ranking Relevance in Yahoo Search (Paper) Yahoo
  9. An Ensemble-Based Approach to Click-Through Rate Prediction for Promoted Listings at Etsy (Paper) Etsy
  10. Learning to Rank Personalized Search Results in Professional Networks (Paper) LinkedIn
  11. Entity Personalized Talent Search Models with Tree Interaction Features (Paper) LinkedIn
  12. In-Session Personalization for Talent Search (Paper) LinkedIn
  13. The AI Behind LinkedIn Recruiter search and recommendation systems LinkedIn
  14. Quality Matches Via Personalized AI for Hirer and Seeker Preferences LinkedIn
  15. AI at Scale in Bing Microsoft
  16. Query Understanding Engine in Traveloka Universal Search Traveloka
  17. The Secret Sauce Behind Search Personalisation GoJek
  18. Food Discovery with Uber Eats: Building a Query Understanding Engine Uber
  19. Neural Code Search: ML-based code search using natural language queries Facebook
  20. Bayesian Product Ranking at Wayfair Wayfair
  21. COLD: Towards the Next Generation of Pre-Ranking System (Paper) Alibaba

Embeddings

  1. Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba (Paper) Alibaba
  2. Embeddings@Twitter Twitter
  3. Listing Embeddings in Search Ranking (Paper) Airbnb
  4. Understanding Latent Style Stitch Fix
  5. Towards Deep and Representation Learning for Talent Search at LinkedIn (Paper) LinkedIn
  6. Vector Representation Of Items, Customer And Cart To Build A Recommendation System (Paper) Sears
  7. Machine Learning for a Better Developer Experience Netflix
  8. Announcing ScaNN: Efficient Vector Similarity Search (Paper) (Code) Google

Natural Language Processing

  1. Abusive Language Detection in Online User Content (Paper) Yahoo
  2. How Natural Language Processing Helps LinkedIn Members Get Support Easily LinkedIn
  3. Building Smart Replies for Member Messages LinkedIn
  4. Smart Reply: Automated Response Suggestion for Email (Paper) Google
  5. SmartReply for YouTube Creators Google
  6. Using Neural Networks to Find Answers in Tables (Paper) Google
  7. A Scalable Approach to Reducing Gender Bias in Google Translate Google
  8. Assistive AI Makes Replying Easier Microsoft
  9. AI Advances to Better Detect Hate Speech Facebook
  10. A State-of-the-Art Open Source Chatbot (Paper) Facebook
  11. A Highly Efficient, Real-Time Text-to-Speech System Deployed on CPUs Facebook
  12. Deep Learning to Translate Between Programming Languages (Paper) (Code) Facebook
  13. Goal-Oriented End-to-End Conversational Models with Profile Features in a Real-World Setting (Paper) Amazon
  14. How Gojek Uses NLP to Name Pickup Locations at Scale GoJek
  15. Give Me Jeans not Shoes: How BERT Helps Us Deliver What Clients Want Stitch Fix
  16. The State-of-the-art Open-Domain Chatbot in Chinese and English (Paper) Baidu
  17. PEGASUS: A State-of-the-Art Model for Abstractive Text Summarization (Paper) (Code) Google
  18. DeText: A deep NLP framework for intelligent text understanding (Code) LinkedIn
  19. Photon: A Robust Cross-Domain Text-to-SQL System (Paper) (Demo) Salesforce
  20. Applying Topic Modeling to Improve Call Center Operations RICOH

Sequence Modelling

  1. Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction (Paper)Alibaba
  2. Search-based User Interest Modeling with Sequential Behavior Data for CTR Prediction (Paper) Alibaba
  3. Learning to Diagnose with LSTM Recurrent Neural Networks (Paper) Google
  4. Deep Learning for Understanding Consumer Histories (Paper) Zalando
  5. Continual Prediction of Notification Attendance with Classical and Deep Networks (Paper) Telefonica
  6. Using Recurrent Neural Network Models for Early Detection of Heart Failure Onset (Paper) Sutter Health
  7. Doctor AI: Predicting Clinical Events via Recurrent Neural Networks (Paper) Sutter Health

Forecasting

  1. Forecasting at Uber: An Introduction Uber
  2. Engineering Extreme Event Forecasting at Uber with RNN Uber
  3. Transforming Financial Forecasting with Data Science and Machine Learning at Uber Uber
  4. Under the Hood of Gojek’s Automated Forecasting Tool GoJek

Computer Vision

  1. Categorizing Listing Photos at Airbnb Airbnb
  2. Amenity Detection and Beyond — New Frontiers of Computer Vision at Airbnb Airbnb
  3. Powered by AI: Advancing product understanding and building new shopping experiences Facebook
  4. Creating a Modern OCR Pipeline Using Computer Vision and Deep Learning Dropbox
  5. How we Improved Computer Vision Metrics by More Than 5% Only by Cleaning Labelling Errors Deepomatic
  6. A Neural Weather Model for Eight-Hour Precipitation Forecasting (Paper) Google
  7. Machine Learning-based Damage Assessment for Disaster Relief (Paper) Google
  8. RepNet: Counting Repetitions in Videos (Paper) Google
  9. Converting Text to Images for Product Discovery (Paper) Amazon
  10. How Disney Uses PyTorch for Animated Character Recognition Disney
  11. Image Captioning as an Assistive Technology (Video) IBM
  12. AI for AG: Production machine learning for agriculture Blue River

Reinforcement Learning

  1. Deep Reinforcement Learning for Sponsored Search Real-time Bidding (Paper) Alibaba
  2. Dynamic Pricing on E-commerce Platform with Deep Reinforcement Learning (Paper) Alibaba
  3. Budget Constrained Bidding by Model-free Reinforcement Learning in Display Advertising (Paper) Alibaba
  4. Productionizing Deep Reinforcement Learning with Spark and MLflow Zynga
  5. Deep Reinforcement Learning in Production Part1 Part 2 Zynga
  6. Building AI Trading Systems Denny Britz

Anomaly Detection

  1. Detecting Performance Anomalies in External Firmware Deployments Netflix
  2. Detecting and Preventing Abuse on LinkedIn using Isolation Forests (Code) LinkedIn
  3. Preventing Abuse Using Unsupervised Learning LinkedIn
  4. Uncovering Insurance Fraud Conspiracy with Network Learning (Paper) Ant Financial
  5. How Does Spam Protection Work on Stack Exchange? Stack Exchange
  6. Auto Content Moderation in C2C e-Commerce Mercari
  7. Anomalies in Oil Temperature Variations in a Tunnel Boring Machine SENER
  8. Using Anomaly Detection to Monitor Low-Risk Bank Customers Rabobank

Graph

  1. Building The LinkedIn Knowledge Graph LinkedIn
  2. Retail Graph — Walmart’s Product Knowledge Graph Walmart
  3. Food Discovery with Uber Eats: Using Graph Learning to Power Recommendations Uber
  4. AliGraph: A Comprehensive Graph Neural Network Platform (Paper) Alibaba
  5. Scaling Knowledge Access and Retrieval at Airbnb Airbnb

Optimization

  1. How Trip Inferences and Machine Learning Optimize Delivery Times on Uber Eats Uber
  2. Next-Generation Optimization for Dasher Dispatch at DoorDash DoorDash
  3. Matchmaking in Lyft Line (Part 1) (Part 2) (Part 3) Lyft
  4. The Data and Science behind GrabShare Carpooling (PAPER NEEDED) Grab
  5. Optimization of Passengers Waiting Time in Elevators Using Machine Learning Thyssen Krupp AG

Information Extraction

  1. Unsupervised Extraction of Attributes and Their Values from Product Description (Paper) Rakuten
  2. Information Extraction from Receipts with Graph Convolutional Networks Nanonets
  3. Using Machine Learning to Index Text from Billions of Images Dropbox
  4. Extracting Structured Data from Templatic Documents (Paper) Google

Weak Supervision

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

Generation

  1. Better Language Models and Their Implications (Paper)OpenAI
  2. Language Models are Few-Shot Learners (Paper) (GPT-3 Blog post) OpenAI
  3. Image GPT (Paper) (Code) OpenAI
  4. Deep Learned Super Resolution for Feature Film Production (Paper) Pixar

Validation and A/B Testing

  1. The Reusable Holdout: Preserving Validity in Adaptive Data Analysis (Paper) Google
  2. Detecting Interference: An A/B Test of A/B Tests LinkedIn
  3. Building Inclusive Products Through A/B Testing (Paper) LinkedIn
  4. Experimenting to Solve Cramming Twitter
  5. Announcing a New Framework for Designing Optimal Experiments with Pyro (Paper) (Paper) Uber
  6. Enabling 10x More Experiments with Traveloka Experiment Platform Traveloka
  7. Large Scale Experimentation at Stitch Fix (Paper) Stitch Fix
  8. Multi-Armed Bandits and the Stitch Fix Experimentation Platform Stitch Fix
  9. Modeling Conversion Rates and Saving Millions Using Kaplan-Meier and Gamma Distributions (Code) Better

Practices

  1. Practical Recommendations for Gradient-Based Training of Deep Architectures (Paper) Yoshua Bengio
  2. Machine Learning: The High Interest Credit Card of Technical Debt (Paper) (Paper) Google
  3. Rules of Machine Learning: Best Practices for ML Engineering Google
  4. On Challenges in Machine Learning Model Management Amazon
  5. Machine Learning in production: the Booking.com approach Booking
  6. 150 Successful Machine Learning Models: 6 Lessons Learned at Booking.com (Paper) Booking
  7. Engineers Shouldn’t Write ETL: A Guide to Building a High Functioning Data Science Department Stitch Fix
  8. Beware the Data Science Pin Factory: The Power of the Full-Stack Data Science Generalist Stitch Fix
  9. Successes and Challenges in Adopting Machine Learning at Scale at a Global Bank Rabobank

Fails

  1. 160k+ High School Students Will Graduate Only If a Model Allows Them to International Baccalaureate
  2. When It Comes to Gorillas, Google Photos Remains Blind Google
  3. An Algorithm That ‘Predicts’ Criminality Based on a Face Sparks a Furor Harrisburg University
  4. It's Hard to Generate Neural Text From GPT-3 About Muslims OpenAI
  5. A British AI Tool to Predict Violent Crime Is Too Flawed to Use United Kingdom

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

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📚 Papers & articles of companies sharing their work on applied data science & machine learning.

License:MIT License