Case Study: ML in Social Media
Explore how to design a scalable machine learning recommendation system for a social media platform with 500 million daily users. Understand data ingestion, feature engineering, hybrid modeling, privacy compliance, and real-time serving to build effective and ethical personalized content recommendations.
We'll cover the following...
Designing machine learning systems at scale requires infrastructure that handles billions of interactions efficiently and ethically. In this lesson, we’ll explore how to create a real-time recommendation engine for social media. Let’s dive in.
Scalable ML recommendation system design challenge
Design a scalable machine learning-based content recommendation system for a large social media platform with 500 million daily active users. The system must:
Provide personalized content recommendations in real time.
Handle high traffic and low latency.
Maintain user privacy.
Scale horizontally.
System requirements:
Scalability design
Low-latency recommendation generation
Privacy considerations
Model performance and personalization
You are asked to draw a flow of the sample system design for such an architecture and implement high-level pseudocode (does not need to be functional) to address the problem statement.
You may jot down your pseudocode here:
Sample answer
Let’s break down the components of how we can tackle this complex problem.
High-level system design
Let’s first explore the high-level system design and the benefits of each tool/framework available at every stage.
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