I developed a full-stack web application designed to simplify on-demand machine learning model deployment using cloud GPUs. The platform provides users with a robust, automated environment to upload, manage, and execute their ML workloads.

Key Features and Architecture

Dynamic GPU Provisioning: Engineered a Go-based backend system to automatically spin up and configure cloud GPU instances with Docker support.
Secure User Authentication: Integrated Supabase authentication (Google, GitHub, and email login) with JWT-based access control for secure API interactions.
Structured Data Management: Built a PostgreSQL schema to track users, deployment templates, and GPU instance metadata.
Intuitive Frontend Interface: Crafted a responsive React dashboard for uploading Docker images and launching ML models with customizable runtime settings.
CI/CD Pipelines: Automated code testing and deployments via GitHub Actions and Jenkins for consistent application updates.
GPU-Ready VM Configuration: Enabled virtual machines to auto-run Docker containers using predefined templates for seamless model execution.
Monitoring & Logging: Deployed Prometheus and Grafana for system metrics, with Loki for centralized log aggregation and troubleshooting.

Random Forest Technology

Write a review for Random Forest Technology

Please fill the form below

Budget

Quality

Schedule

Communication