Architecture

Model Stack

Type	Description
Base Models	Pre-trained models for common tasks
Fine-tuned Models	Custom models trained on CRED data
Ensemble Models	Combined models for improved accuracy
Real-time Models	Low-latency inference models

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Data Processing Pipeline

Data Sources

• Structured Data - Databases, APIs, CSV files
• Unstructured Data - Text documents, images, audio
• Real-time Data - Streaming data from various sources
• Historical Data - Time-series data for training

Processing Stages

┌─────────────┐    ┌─────────────┐    ┌─────────────┐
│   Ingest    │ →  │  Preprocess │ →  │  Features   │
│   Data      │    │   & Clean   │    │  Engineer   │
└─────────────┘    └─────────────┘    └─────────────┘
                                             │
                                             ▼
┌─────────────┐    ┌─────────────┐    ┌─────────────┐
│   Deploy    │ ←  │  Validate   │ ←  │    Train    │
│   Model     │    │   & Test    │    │    Model    │
└─────────────┘    └─────────────┘    └─────────────┘

1. Data Ingestion - Collect and validate input data
2. Preprocessing - Clean, normalize, and transform data
3. Feature Engineering - Extract relevant features
4. Model Training - Train models on processed data
5. Validation - Test model performance and accuracy
6. Deployment - Serve models in production environment