🎯 Multimodal AI Learning Path
Master ImageBind and multimodal search from basics to production
Setting Up ImageBind Environment
Learn how to install and configure ImageBind with Docker, set up your development environment, and run your first multimodal embedding.
Building Your First API Endpoint
Create a FastAPI endpoint that accepts images and returns embeddings using ImageBind. Includes error handling and response formatting.
Implementing Vector Search with FAISS
Build a similarity search system using FAISS. Learn to index embeddings, perform searches, and optimize for performance.
Cross-Modal Search Implementation
Create a system that can search for images using text queries, audio using images, and any modality combination using ImageBind.
MongoDB Integration for Metadata
Design and implement a MongoDB schema for storing multimodal content metadata, with efficient queries and aggregation pipelines.
Production Deployment with Docker
Deploy your multimodal search API to production using Docker Compose, with monitoring, logging, and scaling considerations.
Advanced Embedding Optimization
Learn advanced techniques for optimizing embedding generation, batch processing, and memory management for large-scale applications.
Building a Complete Web Interface
Create a React frontend that connects to your API, with file upload, search functionality, and real-time results visualization.
📚 Progressive Learning
Each tutorial builds upon the previous ones, creating a complete multimodal search system by the end.
🛠️ Hands-On Practice
Every challenge includes practical code examples, exercises, and real-world implementations.
🚀 Production Ready
Learn not just the concepts, but how to deploy and scale multimodal AI systems in production.