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Project Overview
Managed and developed an in-house Kubernetes-based solution for machine learning model development, retraining, testing, and deployment.
Key Responsibilities
- Wrote YAML scripts and shell scripts for platform installation.
- Developed user and project-specific sharing functionalities using Kubernetes RBAC.
- Resolved platform errors and improved reliability through chaos engineering and issue tracking.
- Created tailored JupyterLab images for machine learning development.
- Managed container images and Helm charts via a dedicated Harbor repository.
Achievements
- Centralized development environment and enhanced GPU resource utilization.
- Streamlined project sharing and provided diverse services within a unified domain and cluster.