ML System Design: Video Search System

Paul Deepakraj Retinraj
32 min read1 hour ago

Architecture for Efficient Video Search using Text Queries

Problem Navigation

Requirements

Business Objective: To design a video search system that recommends relevant videos based on user-provided text queries.

Real-world Examples:

  • Search Engines: Platforms like Google, YouTube, and Bing use text-based video search to help users find videos related to their search queries.
  • Social Media: Platforms like TikTok and Instagram allow users to search for videos using keywords or hashtags.
  • Video Streaming Services: Platforms like Netflix and Amazon Prime Video use text search to recommend videos based on user queries and viewing history.
  • E-commerce: Online retailers use video search to help customers find product demonstrations, reviews, and tutorials.
  • Education Platforms: Platforms like Khan Academy and Coursera use text search to allow students to find educational videos on specific topics.

Scope (Features and Use Cases):

Features:

  • Accurate text-based video retrieval: Retrieve videos that are semantically relevant to the user’s text query.
  • Robust query

--

--

Paul Deepakraj Retinraj
Paul Deepakraj Retinraj

Written by Paul Deepakraj Retinraj

Software Architect at Salesforce - Machine Learning, Deep Learning and Artificial Intelligence. https://www.linkedin.com/in/pauldeepakraj/

No responses yet