CRED AI Models

Overview

CRED's AI models form the core intelligence layer of our platform, providing advanced machine learning capabilities for predictive analytics, data processing, and intelligent automation. Our models are designed to handle complex business scenarios with high accuracy and performance.

Model Architecture

Core Models

Predictive Analytics Model - Forecasting and trend analysis
Data Matching Model - Entity resolution and data deduplication
Natural Language Processing - Text analysis and understanding
Computer Vision - Image and document processing
Recommendation Engine - Personalized content and suggestions

Model Stack

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

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

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

Model Development

Getting Started

Clone Repository

git clone https://github.com/credinvest/cred-model.git
cd cred-model

Set Up Environment

python -m venv venv
source venv/bin/activate  # Linux/Mac
# or
venv\Scripts\activate  # Windows

Install Dependencies
```
pip install -r requirements.txt
```

Configure Environment

cp .env.example .env
# Configure API keys and database connections

Training Pipeline

# Example training script
from cred_model import ModelTrainer

trainer = ModelTrainer(
    model_type="predictive_analytics",
    data_path="data/training_data.csv",
    config_path="config/model_config.yaml"
)

# Train model
model = trainer.train()

# Evaluate performance
metrics = trainer.evaluate(model)

# Save model
trainer.save_model(model, "models/predictive_model_v1.pkl")

Model Evaluation

Accuracy Metrics - Precision, recall, F1-score
Performance Metrics - Inference time, memory usage
Business Metrics - ROI, user satisfaction, error rates
A/B Testing - Compare model versions in production

Model Deployment

Serving Infrastructure

Model API - RESTful endpoints for model inference
Batch Processing - Scheduled model runs for large datasets
Real-time Inference - Low-latency predictions
Model Versioning - Track and manage model versions

Deployment Environments

Development - https://model-api-dev.credplatform.com/
Staging - https://model-api-staging.credplatform.com/
Production - https://model-api.credplatform.com/

Monitoring

Model Performance - Track accuracy and drift
System Health - Monitor API response times
Data Quality - Validate input data quality
Alerting - Notify on model degradation

API Usage

Authentication

import requests

headers = {
    'Authorization': f'Bearer {api_token}',
    'Content-Type': 'application/json'
}

Model Inference

# Example API call
response = requests.post(
    'https://model-api.credplatform.com/predict',
    headers=headers,
    json={
        'model_id': 'predictive_analytics_v1',
        'input_data': {
            'feature1': 100,
            'feature2': 'category_a',
            'feature3': [1, 2, 3]
        }
    }
)

predictions = response.json()

Batch Processing

# Upload data for batch processing
with open('data/batch_input.csv', 'rb') as f:
    response = requests.post(
        'https://model-api.credplatform.com/batch',
        headers=headers,
        files={'file': f},
        data={'model_id': 'predictive_analytics_v1'}
    )

job_id = response.json()['job_id']

# Check job status
status_response = requests.get(
    f'https://model-api.credplatform.com/jobs/{job_id}',
    headers=headers
)

Performance Optimization

Model Optimization

Quantization - Reduce model size and inference time
Pruning - Remove unnecessary model parameters
Distillation - Train smaller models from larger ones
Caching - Cache frequent predictions

Infrastructure Optimization

GPU Acceleration - Use GPUs for faster inference
Load Balancing - Distribute requests across multiple instances
Auto-scaling - Scale resources based on demand
CDN - Cache model artifacts closer to users

Data Science Tools

Development Environment

Jupyter Notebooks - Interactive model development
BigQuery - Large-scale data analysis
MLflow - Model lifecycle management
DVC - Data version control

Monitoring Tools

Metabase - Model performance dashboards
Streamlit - Interactive model demos
Grafana - System monitoring and alerting

Best Practices

Model Development

Data Validation - Always validate input data
Feature Engineering - Create meaningful features
Cross-validation - Use proper validation techniques
Documentation - Document model assumptions and limitations

Deployment

Version Control - Track all model versions
Testing - Test models thoroughly before deployment
Monitoring - Monitor model performance continuously
Rollback - Have rollback procedures ready

LinkedIn Data Updates

Overview

When we receive new data from LinkedIn, we need to run two specific jobs to ensure updates are properly propagated throughout the system:

LinkedIn Update Job - Rebuilds only the LinkedIn models
Downstream Refresh Job - Updates dependent models that rely on LinkedIn data

LinkedIn Update Job

The LinkedIn update job rebuilds only the LinkedIn models with the latest data received. It runs every Wednesday as an automated scheduled job: LinkedIn Update Job

Downstream Refresh Job

The downstream refresh job updates the main dependent models that rely on LinkedIn data. It requires manual trigger: Downstream Refresh Job

Affected Models: - PersonJob, PersonEducation, PersonLanguage, PersonSkill, PersonInterest, PersonCertification, PersonCategory, PersonAudienceSkill, PersonAddress, PersonFieldValue - all_addresses, address_chatgpt_components, all_addresses_distinct, all_addresses_distinct_components, all_addresses_matched_components, all_company_hq_addresses, address_regionid, address_sources_prebuild, company_office_location_normalized, CompanyAddress, person_education_address, ProfileAddress, address_prebuild, Address - LICompanyFields, LIPersonFields, CompanyFieldValue - PersonSearch, CompanySearch - CompanyFieldsPrebuild

Weekly Jobs vs Manual Refresh

We already have weekly jobs that propagate these updates gradually across all models. The manual downstream refresh job is just a shortcut to speed things up when we specifically want to refresh everything that's closely linked to LinkedIn data.

Workflow

When new LinkedIn data arrives:

Automatic: LinkedIn Update Job runs (scheduled for Wednesdays)
Manual: Trigger Downstream Refresh Job if immediate propagation is needed
Alternative: Wait for weekly jobs to gradually propagate updates

Support

Documentation: This wiki and inline code documentation
Slack: #data_science channel for team discussions
Issues: GitHub issues for bug reports and feature requests
Email: Contact the data science team for urgent issues

For technical questions, check the Data Architecture or ask in #data_science Slack channel.