Linkedin, X, Stripe & +300 Startups ML System Designs for AI founders

How the top tech companies design ML systems that drive billions in value

Hey everybody, welcome to Product Market Fit.

Today I wanted to share a compilation to help AI founders build their products.

Most ML content online is academic or marketing stuff.

What founders and builders actually need are real production examples from companies that have already solved the problems you’re facing: scaling recommendations, detecting fraud, ranking content, predicting demand.

This resource gives you exactly that: 300 plus case studies of ML systems deployed in production, complete with architecture, evaluation, and rollout details. You see how world-class teams design, validate, and ship ML that drives revenue, retention, and margin impact.

Who’s inside
Netflix, Airbnb, Stripe, Uber, DoorDash, LinkedIn, Spotify, Microsoft, Meta, Walmart, Etsy, Zillow, Instacart, Lyft, Pinterest, Salesforce, Expedia, GitHub, Apple, Monzo, Grab, Delivery Hero and more.

What you’ll find

• Search & Recommendations: Spotify Track Neural Recommender, Etsy multi-task canonical ranker, Airbnb diversified ranking

• Fraud & Trust: Stripe Radar, Uber anomaly detection, BlaBlaCar fraud prevention

• Forecasting & Pricing: DoorDash holiday demand predictions, Lyft causal forecasting, Expedia flight price forecasting

• Generative AI: GitHub Copilot, Stitch Fix expert-in-the-loop, Nextdoor engagement with GenAI

Check it out with the free trial (no strings attached), or jump straight into premium if you’re ready for the full scoop👇