# Centralized RTB ML Platform

Factored built a centralized ML platform for RTB that cut costs by 30%, improved data quality, and standardized CI/CD workflows.

## Key Takeaways:

#### Identifying Strategic Opportunities.

The platform team faced three critical challenges:

- **Data Quality Issues.** The need to detect missing or corrupted data before it was used in ML models.
- **High Snowflake Costs.** Inefficient SQL queries and unused data led to excessive cloud storage and computation costs.
- **CI/CD Standardization.** Multiple CI/CD tools required migration and standardization to streamline workflows.

#### Building a Platform to Elevate AI Function.

- **Data Quality Framework.** Built a monitoring system using Victoria Metrics to track data integrity, with PagerDuty for real-time notifications.
- **Snowflake Cost Optimization.** Implemented query profiling and performance tuning.
- **CI/CD Migration to GitHub Actions.** Migrated and standardized CI/CD pipelines using GitHub Actions, ensuring a more consistent and automated deployment process.

#### Lowering Cost by 30%.
- Improving overall efficiency.
  - Preventing data errors.
  - Optimizing query performance.
  - Optimized data usage.