How to Host MLFlow on AWS: A Complete Guide to Architecture and AWS Components

Table of Contents

• Introduction
• Prerequisites
• AWS Components Required
  • EC2 Instances
  • RDS Database
  • S3 Bucket
  • VPC and Security Groups
  • IAM Roles
• Architecture Overview
• Step-by-Step Guide
  • Setting Up EC2 Instances
  • Configuring RDS Database
  • Creating and Configuring an S3 Bucket
  • Configuring VPC and Security Groups
  • Setting Up IAM Roles
  • Installing and Running MLFlow
• Monitoring and Maintenance
• Conclusion

Introduction

Hosting machine learning workflows efficiently is crucial for any modern enterprise. MLFlow is a platform that manages the machine learning lifecycle, including experimentation, reproducibility, and deployment. This guide will walk you through how to set up and host MLFlow on AWS, focusing on the architectural components and the services offered by AWS that are essential for a robust deployment.

Prerequisites

Before proceeding, make sure you have:

• An AWS account
• Basic familiarity with AWS services
• AWS CLI installed and configured
• Python and MLFlow installed on your local machine

AWS Components Required

EC2 Instances

Amazon EC2 (Elastic Compute Cloud) provides the computational horsepower for your MLFlow setup. You'll need an EC2 instance to serve the MLFlow UI and another to run the tracking server.

RDS Database

Amazon RDS (Relational Database Service) is used to store metadata and metrics. PostgreSQL or MySQL can be utilized as the underlying database.

S3 Bucket

Amazon S3 will be used to store the machine learning artifacts. These include models, parameters, and other related files.

VPC and Security Groups

A Virtual Private Cloud (VPC) and associated security groups should be configured for isolation and access control.

IAM Roles

Identity and Access Management (IAM) roles should be configured for resource permissioning and API access between AWS services.

Architecture Overview

• EC2 Instance 1: Hosts MLFlow UI
• EC2 Instance 2: Runs MLFlow tracking server
• RDS: Stores metadata and metrics
• S3 Bucket: Stores machine learning artifacts
• VPC: Networks all components
• Security Groups: Regulate inbound/outbound traffic
• IAM Roles: Grants permissions

Step-by-Step Guide

Setting Up EC2 Instances

1. Navigate to EC2 Dashboard
2. Launch Instance
3. Choose AMI: Amazon Linux 2 LTS
4. Instance Type: t2.medium should suffice for a moderate load.
5. Configure Security Group: Allow inbound HTTP/HTTPS.
6. Launch and SSH: SSH into each instance to install dependencies.

Configuring RDS Database

1. Navigate to RDS Dashboard
2. Create Database
3. Choose Engine: PostgreSQL or MySQL
4. Configure: Assign VPC, security group, and IAM role.
5. Initialize Tables: SSH into one EC2 instance and run SQL scripts to set up tables.

Creating and Configuring an S3 Bucket

1. Navigate to S3 Dashboard
2. Create Bucket
3. Configure Bucket Policy: Allow access from EC2 instances.

Configuring VPC and Security Groups

1. Navigate to VPC Dashboard
2. Create VPC: Define IP CIDR block and attach all resources.
3. Create Security Groups: For EC2 and RDS.

Setting Up IAM Roles

1. Navigate to IAM Dashboard
2. Create Role: Grant permissions to access RDS and S3.
3. Attach to Resources: Attach the IAM role to EC2 instances.

Installing and Running MLFlow

1. SSH into EC2 Instance: Pick the one for the tracking server.
2. Install MLFlow: Run pip install mlflow.
3. Initialize Server: mlflow server --backend-store-uri <RDS_URI> --default-artifact-root s3://<Your-Bucket-Name>/ --host 0.0.0.0

Monitoring and Maintenance

Use Amazon CloudWatch to monitor EC2 and RDS performance metrics. Set up alerts for high CPU utilization or low available storage.

Conclusion

Hosting MLFlow on AWS involves several AWS components like EC2, RDS, S3, VPC, and IAM. Following this guide ensures that you have a robust, scalable, and secure setup for managing your machine learning workflows. Happy experimenting!