GPU-Enabled Kubernetes Platform for ML Workloads

Designed and implemented a production-grade Kubernetes platform with GPU support, GitOps automation, and comprehensive observability, transforming a fragile infrastructure into a reliable ML platform.

10+/day

Deployment Frequency

70x faster (from 1/week)

30 minutes

Lead Time for Changes

96% reduction (from 2 weeks)

15 minutes

Mean Time to Recovery

94% reduction (from 4 hours)

The Challenge

The client's ML platform was built on manually-provisioned infrastructure that had grown organically over two years. The team faced operational challenges that consumed significant engineering resources and limited their ability to scale GPU workloads.

Manual deployment processes requiring SSH access and direct cluster manipulation

No Infrastructure as Code — configuration existed only in the AWS console

Inefficient GPU resource utilization at approximately 30%

Unreliable deployments with a 20% failure rate

Extended recovery times averaging 4 hours per incident

No standardized approach to scaling GPU workloads for ML inference

Our Approach

Designed and implemented a complete infrastructure rebuild using modern cloud-native practices. The solution prioritized automation, reliability, and GPU-optimized workload management with a parallel migration strategy.

AWS EKSTerraformTerragruntFlux CDNVIDIA Device PluginDCGM ExporterPrometheusGrafana

Implementation Timeline

Total Duration: 6 weeks end-to-end implementation

Foundation

2 weeks

Multi-AZ VPC architecture with private subnets
EKS cluster with IRSA (IAM Roles for Service Accounts)
Remote state management with S3 and DynamoDB locking
Reusable Terraform modules for VPC and EKS

GitOps Pipeline

2 weeks

Flux CD bootstrap with GitHub integration
Helm controller for package management
Kustomize overlays for environment-specific configuration
Automated PR validation and deployment previews

GPU Scheduling

1 week

NVIDIA device plugin deployment via GitOps
GPU node pools with g4dn.xlarge and g4dn.2xlarge instances
Spot instance integration for batch workloads
Taints and tolerations for GPU-exclusive scheduling

Observability

1 week

Prometheus with GPU metrics (DCGM) integration
Grafana dashboards for cluster and GPU monitoring
Alertmanager with escalation policies
Kubecost integration for cost visibility

Technical Architecture

Cloud-native architecture with GitOps-driven deployments, GPU-optimized node scheduling, and comprehensive observability for ML workloads.

AWS EKS with managed node groups

Terraform + Terragrunt for IaC

Flux CD for GitOps automation

NVIDIA Device Plugin for GPU scheduling

DCGM Exporter for GPU telemetry

Prometheus + Grafana for monitoring

Results & Impact

10+/day

Deployment Frequency

70x faster (from 1/week)

30 minutes

Lead Time for Changes

96% reduction (from 2 weeks)

15 minutes

Mean Time to Recovery

94% reduction (from 4 hours)

<2%

Failed Deployment Rate

90% reduction (from 20%)

75%

GPU Utilization

2.5x improvement (from 30%)

99.9%

Infrastructure Uptime

Production-grade (from 80%)

Business Benefits

Self-service deployments — Development teams deploy independently through Git

Complete audit trail — All changes tracked in version control

Automated scaling — GPU resources scale based on workload demand

Proactive monitoring — Issues detected and alerted before user impact

Spot instances for non-critical workloads reduced compute costs by 60%

Improved GPU utilization eliminated $3K+/month in idle resources

“The platform transformation exceeded our expectations. Deployments that used to take weeks now happen in minutes, and our ML team can finally scale GPU resources on demand. The GitOps workflow has fundamentally changed how we ship software.”

Engineering Lead

ML Platform Team

Key Learnings

GitOps requires enforcement, not just tooling — making the bastion read-only was essential

GPU scheduling has quirks — driver version mismatches caused mysterious failures

Start with observability — deploying monitoring early proved invaluable during migration

Show the cost — adding cost visibility early revealed $3K/month in idle GPU instances

Recommendations

Deploy monitoring stack before anything else for debugging during migration

Pin GPU driver and device plugin versions explicitly

Use spot instances for batch workloads with proper interruption handling

Implement cost visibility early to identify waste

KubernetesGPUGitOpsAWS EKSTerraformML Infrastructure