🚀 Building a Secure MLOps Pipeline with Kubernetes on AWS

Machine Learning models are only as powerful as the systems that deploy and manage them. In this blog, we walk through the creation of a production-ready MLOps pipeline using MLflow, Docker, Kubernetes (EKS), Terraform, and security tools like Trivy and Kube-bench. We’ll also touch on monitoring and observability using Prometheus and Grafana.

Whether you’re an ML engineer, DevOps engineer, or working at the intersection of both — this guide will give you insight into setting up a robust, secure, and automated MLOps environment.

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🧱 Tech Stack Overview

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⚙️ Architecture Summary

The pipeline is designed with CI/CD-first thinking, infrastructure-as-code, and zero-trust Kubernetes security principles.

Key Capabilities:

• 📦 Model versioning and artifact storage via MLflow

• 🔁 CI/CD automation of model training → validation → deployment

• 🔐 Kubernetes hardening with RBAC, PodSecurity policies, image scanning

• 📊 Real-time monitoring of model inference services

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🛠️ Step-by-Step Breakdown

1. Infrastructure Setup with Terraform

Using Terraform modules, we created:

• A secure VPC and subnets

• EKS cluster with autoscaling node groups

• IAM roles for fine-grained permissions

• Helm charts for MLflow, Prometheus, and Grafana deployments

module "eks" {
  source          = "terraform-aws-modules/eks/aws"
  cluster_name    = "mlops-cluster"
  node_groups     = { ... }
}

2. Model Training and Tracking with MLflow

ML engineers pushed training runs into MLflow Tracking Server, hosted on Kubernetes with persistent volume for backend storage (S3 or EBS).

• Artifacts: Trained models, plots

• Params & Metrics: Model accuracy, loss, etc.

Models were promoted to production using MLflow Model Registry.

3. Dockerizing and Serving Models

Each model was:

• Wrapped in a FastAPI or Flask-based inference server

• Dockerized and published to Amazon ECR

• Deployed as K8s Deployments + Services

FROM python:3.10
COPY . /app
RUN pip install -r requirements.txt
CMD ["uvicorn", "main:app"]

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4. CI/CD for Model Deployment

GitHub Actions triggered:

• Model retraining on new data

• Docker build & push

• Kubernetes deployment via Helm or kubectl

jobs:
  deploy:
    steps:
      - name: Build & Push Docker
      - name: Apply K8s Manifests

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5. Securing the Kubernetes Cluster

We hardened the cluster using:

Example: Trivy CI job failed builds if vulnerabilities exceeded a threshold.

trivy image myapp:latest --severity CRITICAL

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6. Observability with Prometheus & Grafana

• Prometheus Operator deployed via Helm

• Collected metrics from ML model services (response time, error rates)

• Custom Grafana dashboards visualized real-time performance

Tracked latency, throughput, and model drift metrics.

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🧪 Results and Benefits

✅ End-to-End Automation: From model training to serving
✅ Improved Security Posture: Compliance with CIS benchmarks
✅ Scalability & Portability: Infrastructure reproducible across accounts
✅ Real-time Monitoring: Proactive model and system health visibility

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🎯 Final Thoughts

MLOps is not just about deploying models; it’s about ensuring reliability, reproducibility, and security at scale. By combining the strengths of Kubernetes, Terraform, and MLflow, we were able to build a battle-tested MLOps pipeline ready for production workloads.

Next Steps: Add Drift Detection, A/B testing, and Canary Deployments.

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🔗 Resources

• MLflow Docs

• Terraform AWS Modules

• Trivy by Aqua

• Kube-bench

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Want help building your MLOps infra or securing your Kubernetes workloads? Let’s connect!