ML Pipelines on Google Vertex AI
Key Results
- CI/CD with GitLab CI and Airflow
- Comprehensive monitoring and deployment
- Scalable production infrastructure
- Real-time analytics integration
The Problem
Multiple ML models for competitor price mapping and product recommendations were being trained and deployed manually, leading to inconsistent model versions, difficult rollbacks, and no systematic monitoring of model performance in production.
The Approach
Designed and implemented end-to-end automated ML pipelines on Google Vertex AI. The infrastructure includes automated data ingestion, feature engineering, model training, evaluation, and deployment stages. GitLab CI handles code integration and testing, while Apache Airflow orchestrates scheduled pipeline runs. Comprehensive monitoring tracks model performance, data drift, and system health.
The Results
The automated pipelines enabled consistent, reproducible model training and deployment with full CI/CD integration. The scalable GCP infrastructure handles growing data volumes, while real-time analytics integration provides immediate visibility into model performance and business impact.