While preparing for job interviews I found some great resources on Machine Learning System designs from Facebook, Twitter, Google, Airbnb, Uber, Instagram, Netflix, AWS and Spotify.
I find this to be a fascinating topic because it’s something not often covered in online courses.
- Using Deep Learning at Scale in Twitter’s Timelines
- Improving engagement on digital ads with delayed feedback
- Lessons Learned at Instagram Stories and Feed Machine Learning
- Powered by AI: Instagram’s Explore recommender system
- Deep Entity Classification: An abusive account detection framework
- New progress in using AI to detect harmful content
- Food Discovery with Uber Eats: Building a Query Understanding Engine
- Food Discovery with Uber Eats: Recommending for the Marketplace
- Food Discovery with Uber Eats: Using Graph Learning to Power Recommendations
- Applying Customer Feedback: How NLP & Deep Learning Improve Uber’s Maps
- Forecasting at Uber: An Introduction
- Using Machine Learning to Predict Value of Homes On Airbnb
- Listing Embeddings in Search Ranking
- Learning Market Dynamics for Optimal Pricing
- Categorizing Listing Photos at Airbnb
- Applying Deep Learning To Airbnb Search
- Discovering and Classifying In-app Message Intent at Airbnb
- An Introduction to AI at LinkedIn
- Fairness, Privacy, and Transparency by Design in AI/ML Systems
- Communities AI: Building Communities Around Interests on LinkedIn
- Preventing abuse using unsupervised learning
- For Your Ears Only: Personalizing Spotify Home with Machine Learning
- How Does Spotify Know You So Well?
In addition, here are some resources on a more general process. Starting with the book Data Science for Business which explains the CRISP-DM (Cross Industry Standard Process for Data Mining).
The process involves six stages:
- Business Understanding
- Data Understanding
- Data Preparation
Here is a more high-level breakdown on how to apply CRISP-DM on AWS.
Facebook also created a video series where they go into depth in how they structure Machine Learning Projects with the Facebook Field Guide to Machine Learning.