Deploying MLflow on Amazon EKS with Terraform

Overview

This blog explains how to deploy MLflow, an open-source machine learning lifecycle platform, on Amazon Elastic Kubernetes Service (EKS) using Terraform for infrastructure automation. The deployment includes persistent storage, external access to the MLflow UI, and integration with core AWS services, all automated using Terraform.

Introduction

In modern machine learning workflows, it’s not enough to train a model, teams must also track experiments, manage model versions, and reproduce results. MLflow is a tool designed to help with exactly that. While MLflow can run locally for development, deploying it on the cloud is essential for production use cases.

Amazon EKS, AWS’s managed Kubernetes service, provides a scalable and resilient environment for running containerized workloads like MLflow. Pairing this with Terraform, a widely adopted Infrastructure-as-Code tool, allows you to automate the entire setup, from network infrastructure to Kubernetes deployments.

In this blog, we will explore how these three technologies, MLflow, Amazon EKS, and Terraform, come together to create a reliable and repeatable platform for managing your machine learning experiments in the cloud.

MLFlow

MLflow is a platform for managing the end-to-end machine learning lifecycle. It helps with:

• Tracking experiments and metrics
• Packaging and saving models
• Deploying models into production
• Organizing runs and results in a UI

Data scientists and ML engineers widely use it to streamline model development and reproducibility.

Why Deploy MLflow on Amazon EKS?

While MLflow can run locally or on a VM, deploying it on Amazon EKS (Elastic Kubernetes Service) provides several advantages:

• Scalability: Amazon EKS automatically scales resources to handle large ML workloads.
• High Availability: Amazon EKS runs across multiple Availability Zones, improving reliability.
• Isolation & Security: Each workload runs in a containerized environment, improving separation and control.
• Integration with AWS services: You can use Amazon S3 for artifact storage, Amazon CloudWatch for logs, and AWS IAM for access control.

Why Use Terraform?

Terraform is an open-source tool that lets you define and provision cloud infrastructure using code. With Terraform, you can:

• Automate the creation of Amazon EKS clusters, networks, and storage.
• Reuse infrastructure modules for consistency.
• Track changes to your cloud environment using version control.
• Re-deploy environments quickly and reliably.

Steps Involved in Deploying MLflow on Amazon EKS with Terraform

Step 1: Set Up Terraform Project Structure

Start by organizing your Terraform files and modules. This typically includes:

• A root configuration (main.tf, variables.tf, outputs.tf)
• Modules for Amazon VPC, Amazon EKS, EBS, and optionally AWS IAM roles
• A directory for Kubernetes manifests (MLflow deployment, service, PVC, etc.)

This structure makes your infrastructure modular and reusable.

Step 2: Provision VPC and Networking

Use Terraform to create:

• A Virtual Private Cloud (VPC)
• Public and private subnets across multiple Availability Zones
• Internet gateway, NAT gateway, and routing tables

This network setup is the foundation for your Amazon EKS cluster and other resources.

Step 3: Deploy Amazon EKS Cluster

Next, create an Amazon EKS cluster using Terraform:

• Define the cluster name, version, and networking settings
• Create a node group using Amazon EC2 instances (e.g., t3.large)
• Assign AWS IAM roles to allow the nodes and Amazon EKS to interact with AWS services

After the cluster is ready, configure kubectl to connect with it.

Step 4: Create an EBS Volume for Persistent Storage

To store MLflow experiment logs (mlruns), provision an EBS volume in the same Availability Zone as your EKS nodes. This volume will be attached to the MLflow pod using a PersistentVolume (PV) and PersistentVolumeClaim (PVC).

Step 5: Apply Kubernetes Manifests

Once the infrastructure is up:

• Create a PersistentVolume and PersistentVolumeClaim to mount the EBS volume
• Deploy the MLflow application using a Kubernetes Deployment
• Expose the MLflow UI through a LoadBalancer Service

These resources run on your Amazon EKS cluster, making MLflow accessible via a public URL.

Step 6: Access MLflow UI

After deployment, get the external IP of the MLflow service. You can open this in a browser to access the MLflow tracking server and start logging experiments.

MLFlow UI

Conclusion

Terraform simplifies infrastructure setup and makes deployments repeatable. This setup is ideal for teams aiming for a reliable, cloud-native MLOps platform.

Drop a query if you have any questions regarding MLflow and we will get back to you quickly.

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