A comprehensive multimodal AI system that leverages visual, textual, and structured data to identify causal drivers of viewer engagement. CAIP combines state-of-the-art deep learning models with causal inference techniques to provide actionable insights for content creators and marketers.
The Cross-Modal Audience Intelligence Platform (CAIP) is designed to:
- Analyze content across multiple modalities (text, image, metadata)
- Identify causal factors that drive audience engagement
- Predict counterfactual outcomes for content modifications
- Recommend optimizations to maximize viewer engagement
- Explain results through transparent causal relationships
CAIP consists of several interconnected components:
┌────────────────┐ ┌─────────────────┐ ┌───────────────────┐
│ Data Pipeline │───▶│ Model Pipeline │───▶│ Causal Pipeline │
└────────────────┘ └─────────────────┘ └───────────────────┘
│ │ │
│ │ │
▼ ▼ ▼
┌────────────────┐ ┌─────────────────┐ ┌───────────────────┐
│ Preprocessing │ │ Fusion Models │ │ Structural Models │
└────────────────┘ └─────────────────┘ └───────────────────┘
│
│
▼
┌─────────────────┐ ┌───────────────────┐
│ API Server │◀───│ RAG System │
└─────────────────┘ └───────────────────┘
- Data Layer: Connectors to various data sources, preprocessing pipelines, and feature engineering
- Model Layer: Visual models (CLIP), text models (RoBERTa), and multimodal fusion models
- Causal Layer: Structural causal models, counterfactual analysis, and causal feature identification
- RAG System: Retrieval-augmented generation for content recommendations and explanations
- Serving Layer: REST API, model serving infrastructure, and containerized deployment
- Python 3.9+
- Docker and Docker Compose (for containerized deployment)
- CUDA-compatible GPU (recommended for training)
-
Clone the repository:
git clone https://github.com/yourusername/cross_modal_audience_platform.git cd cross_modal_audience_platform -
Set up the environment and install dependencies:
./run_local.sh setup
-
Run tests to verify your installation:
./run_local.sh test
CAIP uses a modular architecture with well-defined interfaces between components. The development workflow is streamlined using the run_local.sh script:
# Run all unit tests
./run_local.sh test
# Run integration tests
./run_local.sh integration
# Run causal tests with specific configuration
./run_local.sh causal --config complex
# Collect training data
./run_local.sh collect --limit 1000
# Fine-tune the model
./run_local.sh finetune --epochs 10
# Start API server locally
./run_local.sh api
# Start API using Docker
./run_local.sh dockerFor more options, run:
./run_local.sh helpCAIP employs a structured approach to causal analysis:
- Feature Identification: Extract relevant features from visual and textual content
- Causal Discovery: Learn the causal graph structure from observational data
- Effect Estimation: Quantify the causal effect of content features on engagement
- Counterfactual Analysis: Predict engagement under hypothetical content modifications
- Recommendation Generation: Suggest content changes to optimize engagement
To fine-tune models on your own data:
-
Collect data:
./run_local.sh collect --source your_data_source --limit 5000
-
Fine-tune the model:
./run_local.sh finetune --config configs/fine_tuning.yaml
-
Evaluate the fine-tuned model:
python scripts/evaluate_model.py --model-path models/saved/fusion_model_best.pt
Once the API server is running, you can interact with it using HTTP requests:
import requests
import json
# Analyze content
response = requests.post(
"http://localhost:5000/api/v1/analyze",
headers={"Authorization": f"Bearer {api_key}"},
json={
"text": "Exciting new product launch!",
"image_url": "https://example.com/product_image.jpg",
"metadata": {"category": "technology", "target_audience": "professionals"}
}
)
# Parse results
results = response.json()
print(json.dumps(results, indent=2)).
├── causal/ # Causal inference modules
├── data/ # Data processing pipeline
│ ├── connectors/ # Data source connectors
│ └── preprocessing/ # Data preprocessing modules
├── evaluation/ # Evaluation metrics and benchmarks
├── models/ # Machine learning models
│ ├── fusion/ # Multimodal fusion models
│ ├── text/ # Text models (RoBERTa)
│ └── visual/ # Visual models (CLIP)
├── notebooks/ # Jupyter notebooks for analysis
├── rag/ # Retrieval-augmented generation
├── serving/ # API and deployment components
│ └── kubernetes/ # Kubernetes deployment configs
├── tests/ # Test suites
│ └── integration/ # Integration tests
├── scripts/ # Utility scripts
├── configs/ # Configuration files
├── requirements.txt # Python dependencies
├── run_local.sh # Local development script
└── README.md # This file
CAIP includes comprehensive test suites:
- Unit Tests: Test individual components
- Integration Tests: Test component interactions
- Causal Tests: Validate causal discovery and inference
- Benchmark Tests: Measure performance and scalability
Run tests using:
./run_local.sh test # Unit tests
./run_local.sh integration # Integration tests
./run_local.sh causal # Causal testsDeploy the API server using Docker:
./run_local.sh dockerFor production deployment on Kubernetes:
kubectl apply -f serving/kubernetes/- Fork the repository
- Create a feature branch (
git checkout -b feature/amazing-feature) - Commit your changes (
git commit -m 'Add some amazing feature') - Push to the branch (
git push origin feature/amazing-feature) - Open a Pull Request
This project is licensed under the MIT License - see the LICENSE file for details.
- Inspired by production systems used at Nielsen
- Built with state-of-the-art open-source libraries and models