Design ML Pipeline

Design an end-to-end ML system architecture for a given problem.

Arguments

$ARGUMENTS - The ML problem to design for (e.g., "recommendation system", "fraud detection", "search ranking", "content moderation")

Workflow

1. Clarify requirements by understanding:

   • What predictions are being made?
   • What is the latency requirement? (real-time vs batch)
   • What is the scale? (QPS, data volume)
   • Who are the consumers of predictions?

2. Load relevant skills based on the problem:

   • Core ML architecture → ml-system-design
   • LLM-based systems → llm-serving-patterns