Artificial Intelligence
Advanced
Multimodal AI System Engineering
Build end-to-end applications combining vision, speech, and textual understanding.
Course Overview
Process cross-modal embeddings, integrate Vision-Language models into RAG pipelines, and build real-time multi-modal streaming workflows.
What You Will Learn
Process image, audio, and textual data with unified multimodal models
Build visual Question Answering (VQA) pipelines using Vision-LLMs
Extract structured JSON data from complex PDF documents and diagrams
Embed multimodal content into vector databases for cross-modal retrieval
Implement real-time audio transcription and speech synthesis flows
Tools & Technologies Used
Python 3.11
Whisper
OpenCV
Qdrant
Pydantic
FastAPI
Structured Curriculum
2 Modules · 4 Lessons · 240 Minutes Total
Module 1
Module 1: Vision-Language Models & Document Parsing
2 lessons
Module 1: Vision-Language Models & Document Parsing
Extract text, tables, and visual evidence from images and PDFs.
-
Vision-LLM Prompting & Schema Extraction
Pass image inputs to Vision models to extract structured JSON data from invoices and charts.
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Multi-Modal Document Parsing Pipelines
Combine OCR, layout detection, and vision models to parse complex PDF layouts.
Module 2
Module 2: Audio Intelligence & Cross-Modal RAG
2 lessons
Module 2: Audio Intelligence & Cross-Modal RAG
Index visual and audio embeddings for multi-modal similarity search.
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Speech-to-Text Transcription with Whisper
Transcribe audio streams with word-level timestamps and speaker diarization.
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Cross-Modal Vector Embeddings in Qdrant
Index joint image-text embeddings (CLIP) for natural language image search.
Practical Project & Capstone Outcome
🚀 Capstone Project
Multimodal Technical Inspector & Audio Summarizer
Construct an application that ingests blueprint images and voice notes, queries a multimodal vector index, and outputs an interactive inspection report.
Prerequisites
- Python intermediate proficiency
- Basic familiarity with REST APIs
Intended Audience
- AI Platform Engineers building multi-modal search and document processing services
- Software Engineers developing vision-aware web applications
Instructor Information
E
Elena Rostova
Course Author & Industry Expert
Elena Rostova is a Senior Multimodal Systems Architect who has built document intelligence and vision-RAG systems for global tech enterprises.
Frequently Asked Questions
Does this course cover local vision models like Llama 3.2 Vision?
Yes, lessons cover both hosted API endpoints and local open-source vision runtimes.