ML Infrastructure Pipeline

Deploying a single ML model is straightforward. Operating a platform that supports multiple models in production — with automated retraining, canary rollouts, and monitoring — requires deliberate infrastructure design. This project is that platform.

Design Principles

1. Infrastructure as Code: Every resource is defined in Terraform. No console clicks, no snowflake servers.
2. GitOps workflow: Argo CD watches the Git repo and reconciles cluster state. If someone manually changes a deployment, Argo reverts it.
3. Model-agnostic: The pipeline doesn’t care what framework produced the model artifact. TensorFlow, PyTorch, scikit-learn — they all go through the same deployment path.

Infrastructure Stack

Compute: AWS EKS (Kubernetes 1.27) with managed node groups. GPU node groups for training, spot instances for batch inference.

Networking: VPC with private subnets for cluster nodes, public subnets for load balancers. All egress through a NAT gateway.

Storage: S3 for model artifacts and training data. EBS CSI driver for persistent volumes during training jobs.

CI/CD: GitHub Actions builds Docker images and pushes to ECR. Argo CD detects new manifests and rolls out to EKS.

Deployment Pipeline

Code push → GitHub Actions → Docker build → ECR push →
Manifest update → Argo CD sync → Canary rollout →
Metrics evaluation → Full rollout or rollback

The canary stage routes 10% of traffic to the new model version for 30 minutes. If error rates or latency exceed thresholds, Argo Rollouts automatically rolls back.

Monitoring

Prometheus + Grafana for system metrics. Custom ML metrics (prediction drift, feature distribution skew) are logged to CloudWatch and trigger alerts when they exceed configured thresholds.

Results

Metric	Value
System uptime	99.9%
Deployment time	50% reduction
Models in production	5+ concurrent
Rollback time	< 2 minutes