ML Platform on Kubernetes

Project Summary

Built a self-service ML platform on Kubernetes enabling 50+ data scientists to independently train, experiment, and deploy models, reducing infrastructure wait time from weeks to minutes.

Problem Statement

• Data scientists waiting 2+ weeks for infrastructure provisioning
• No standardized way to deploy models to production
• Experiment tracking scattered across notebooks and spreadsheets
• GPU resources underutilized due to manual allocation

System Architecture

Kubernetes-native platform using Kubeflow for ML pipelines, MLflow for experiment tracking, and custom operators for resource management. Terraform for infrastructure-as-code, Argo for workflow orchestration, and a self-service portal built with React.

Model & Approach

• Designed template-based system for common ML workflows
• Built custom Kubernetes operators for GPU scheduling optimization
• Implemented multi-tenant isolation with namespace-based separation
• Created unified CLI and web interface for consistent user experience

MLOps & Deployment

• GitOps-based deployment with automated CI/CD for model artifacts
• Centralized model registry with versioning and lineage tracking
• Autoscaling based on inference load with custom metrics
• Cost attribution and chargeback per team/project
• Automated compliance checks for model governance

Results & Impact

Lessons Learned

• Self-service requires extensive documentation and training
• Platform adoption depends on reducing friction, not adding features
• Cost visibility drives responsible resource usage
• Building for the 80% use case enables faster iteration