11-12: Multimodal Processing Pipelines
The economics of integrating Computer Vision, Audio Transcription (Whisper), and visual reasoning into text-based systems.
🎯 What You'll Learn
- ✓ Quantify video ingestion compute costs
- ✓ Model image-recognition API margins
- ✓ Optimize transcription architecture
The Data Density Problem
Sending a single HD image to GPT-4o-vision costs 2-3x more tokens than sending a page of text. Processing a 5-minute video by breaking it into frames is economically devastating at B2C scale.
Multimodal pipelines require aggressive preprocessing. Instead of sending raw audio to an LLM, you send it to a specialized, cheap transcription model (Whisper on edge compute) and only forward the text to the expensive LLM.
Architectural filtering—downsampling images, extracting keyframes, isolating audio tracks—is the only way to retain margins in multimodal applications.
The interval at which a video is sampled before being sent to an API.
The drop in product gross margin when users upload massive audio/video files compared to text.
Audit the input validation on any feature accepting images or documents.
Action Items
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Module Syllabus
Lesson 1: The Data Density Problem
Sending a single HD image to GPT-4o-vision costs 2-3x more tokens than sending a page of text. Processing a 5-minute video by breaking it into frames is economically devastating at B2C scale.Multimodal pipelines require aggressive preprocessing. Instead of sending raw audio to an LLM, you send it to a specialized, cheap transcription model (Whisper on edge compute) and only forward the text to the expensive LLM.Architectural filtering—downsampling images, extracting keyframes, isolating audio tracks—is the only way to retain margins in multimodal applications.
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