Project Overview
For four years, I managed the operation, maintenance, and enhancement of a client’s AI platform, earning more than KRW 150 million annually. This included transitioning the “Mesos” infrastructure to “Kubernetes”, developing automation tools, and ensuring the platform's seamless performance.
- Developed 5+ automated monitoring tools (Python, Node.js, Slack API).
- Resolved over 50 infrastructure issues, enhancing system stability.
- Generated 1,000+ automated reports, saving significant time and effort.
- Handled 100+ servers
Unique Challenges and Solutions
- Transition from Mesos to Kubernetes:
- Migrated services to Kubernetes clusters, improving scalability and reliability.
- Designed infrastructure strategies that minimized downtime during the transition.
- Microservices Architecture (MSA) with Spring Cloud:
- Rebuilt monolithic components into Spring Cloud-based microservices.
- Streamlined inter-service communication, enhancing system modularity and performance.
- AI Learning Feature Enhancements:
- Developed new AI training features to meet client-specific needs.
- Optimized GPU usage for faster and more efficient training workflows.
Key Responsibilities
1. Infrastructure Management
- Monitored and maintained clusters, logs, and hardware status.
- Performed NAS evaluations and implemented regular cleanup tasks.
- Improved MongoDB performance with optimized indexing strategies.
2. Automated Tools and Reports
- Created monitoring scripts to detect and resolve infrastructure anomalies.
- Reduced reporting time from one hour to 20 minutes with automation.
3. Service Maintenance
- Regularly reviewed and cleaned backend and frontend logs.
- Supported troubleshooting for PaaS servers and Docker Builder issues.
4. QA and Deployment Automation
- Conducted comprehensive QA for IaaS, PaaS, and other services.
- Automated repetitive QA tasks using scripts for consistent deployment quality.
Achievements
- Stability: Maintained a 99.9% uptime across three years, ensuring client trust.
- Efficiency: Automated key operations, saving over 1,500 hours annually.
- Scalability: Transitioned to Kubernetes and MSA, laying the groundwork for future growth.
- Client Impact: Enabled faster AI model training and deployment, directly contributing to the client's business outcomes.
Reflection
This project highlighted the importance of balancing operational stability with innovation. Transitioning from Mesos to Kubernetes and adopting Spring Cloud-based microservices were significant milestones that required careful planning and execution. By automating processes and focusing on client-centric improvements, I ensured the AI platform remained robust, scalable, and efficient.
- Related Media ( In Korean language)
