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In this ML System Design video, we ask a Senior Machine Learning Engineer from Meta to design a ranking and recommendation system for Instagram. He focuses on increasing user engagement by optimizing post suggestions from both friends and content creators, aiming to boost daily active users and session times by implementing AI in clever ways. Our guest explains the model's functional requirements, emphasizing predicting engagement actions like likes, comments, and views. He also covers the importance of MLOps tools for analytics, monitoring, and alerts to ensure the system's effectiveness and reliability.

Chapters (Powered by ChapterMe)
00:00 Designing Instagram's Ranking Model
03:24 ML Model for Instagram Metrics
08:33 ML Pipeline Nonfunctional Requirements
10:22 Monetization Through Ads
12:03 ML Pipeline Stages Overview
19:18 Pretrained Embeddings for Interaction Analysis
24:04 Comprehensive Model Pipeline Strategy
31:23 Collaborative Filtering for Efficient Representation
33:13 TwoTower Network for Data Filtering
38:43 ML Maturity & AUC Curve Analysis
44:58 Microservices for Continuous Learning and Scaling

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