Case Study: ML in Social Media

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.

1. Data ingestion layer:

   1. Apache Kafka is for real-time data streaming. Kafka’s real-time data ingestion and event streaming capabilities make it indispensable for capturing and relaying massive user interaction data at scale. Its durability and fault tolerance ensure reliability, even under high traffic conditions.

   2. Topics:

      1. User interactions

      2. Content metadata

      3. User profile updates

2. Data processing:

   1. Apache Spark is for distributed data processing. Spark’s distributed, in-memory computation allows for processing large-scale datasets efficiently. It is especially valuable for batch and real-time data pipelines, enabling complex feature engineering, aggregations, and iterative machine learning workflows important for personalized recommendations.

   2. Feature engineering pipeline:

      1. User interaction history

      2. Content similarity ...