System Design Example: Scalable Recommendation Platform

Here’s a summary of the infrastructure-focused ML system design walkthrough. We are building a robust, scalable recommendation platform to support multiple product teams, focusing on infrastructure needs such as latency, reliability, and modularity.

• We design for scalability and flexibility, supporting 10M+ daily predictions across various surfaces (e.g., homepage, search, email), with <50ms latency for real-time use cases and batch support for offline use.
• We architect the system with modular components: batch and streaming feature pipelines, a centralized feature store, a model training platform, a model registry, and scalable inference servers, all integrated with monitoring and alerting.
• We implement tiered feature computation (long-term, medium-term, real-time) using tools like Spark and Flink, and store user, item, and contextual features consistently to power accurate and fresh predictions.
• We support multiple modeling strategies, including two-tower models, matrix factorization, and DNNs, and use a two-stage architecture with candidate generation followed by precise ranking.
• We deploy models reliably using CI/CD pipelines, blue-green deployments, and shadow testing, and maintain performance through A/B testing, autoscaling servers, inference optimizations, and drift detection.

This approach ensures we build ML systems that are production-ready, highly available, and adaptable to the evolving needs of diverse teams.

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