End-to-End ML Pipeline Automation

Developed an end-to-end machine learning pipeline on Google Cloud Platform (GCP) utilizing Docker containers, ensuring scalable and efficient deployment of churn prediction models. The pipeline orchestrated data ingestion, model training, and deployment in a robust, cloud-native environment.

Performed in-depth analysis of customer behavior and churn patterns by leveraging Vertex AI for large-scale data processing, feature engineering, and model training, improving the model’s ability to predict potential customer churn with higher accuracy.

Designed and deployed an intuitive user interface using Streamlit, allowing non-technical stakeholders to visualize churn insights and interact with model predictions in real-time.

Automated the entire CI/CD pipeline with Cloud Build and Cloud Run, integrating Docker containerization to streamline model training, testing, and deployment processes, ensuring continuous updates and scalability of the churn prediction solution.

Integrated version control for machine learning models via Vertex AI Model Registry, enabling efficient model management, versioning, and seamless rollback for model updates, ensuring model governance and traceability across various stages of deployment.