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.
