ML Model Monitoring with Prometheus and Grafana on AWS

A step-by-step guide to monitoring ML models with Prometheus and Grafana on AWS.

Monitoring with Prometheus and Grafana on AWS

Hey there, tech explorers! Have you ever launched a machine learning (ML) model into the wild and wondered, “How’s it doing out there?” Well, wonder no more! We’re about to embark on a journey through the enchanting lands of Prometheus and Grafana, where we’ll monitor our ML models in real-time on the mystical realm of Amazon Web Services (AWS). So, grab your potions (or coffee) and let’s dive into this magical guide to ML model monitoring, DevOps style!

Table of Contents
1. Unveiling the Magic of ML Model Monitoring
2. Why Prometheus and Grafana? The Dynamic Duo
3. Setting Up Camp: AWS Environment and Model Deployment
4. Unleash Prometheus: Real-time Monitoring
5. Grafana: Painting Metrics into Enchanting Dashboards
6. The Enchanted Scroll: DevOps Automation with CI/CD
7. Forging Shields: AWS Security and Scalability

1. Unveiling the Magic of ML Model Monitoring

Picture this: you’ve trained and deployed your marvelous ML model. But the adventure doesn’t stop there. Monitoring is the mystical key to ensuring your models stay accurate and relevant as they navigate the ever-changing landscapes.

2. Why Prometheus and Grafana? The Dynamic Duo

Let’s talk about our dynamic duo — Prometheus and Grafana. Prometheus swoops in to collect and store metrics from your models, while Grafana, the grand storyteller, turns these metrics into captivating visualizations.

3. Setting Up Camp: AWS Environment and Model Deployment

First things first, let’s set up camp in the AWS wilderness. We’ll deploy a brave ML model, using the Amazon SageMaker as our trusty steed.

Step 1: Unveil Your AWS CLI Superpowers
Equip yourself with the AWS Command Line Interface (CLI) by following the mystical guide: AWS CLI Installation Guide:

https://docs.aws.amazon.com/cli/latest/userguide/cli-configure-quickstart.html

Step 2: Summon Your Model on Amazon SageMaker
1. Prepare your spell (code).
2. Summon an Amazon SageMaker instance with your spell:

aws sagemaker create-notebook-instance - notebook-instance-name my-notebook - instance-type ml.t2.medium - role-arn arn:aws:iam::YOUR-ACCOUNT-ID:role/service-role/AmazonSageMaker-ExecutionRole-YYYYMMDDTHHMMSS

3. Venture into the SageMaker Jupyter Notebook interface.

4. Unleash Prometheus: Real-time Monitoring
Step 1: Forge Your Prometheus Arsenal on AWS
1. Enchant an Amazon EC2 instance:

aws ec2 run-instances - image-id ami-xxxxxxxxxxxxxxxxx - count 1 - instance-type t2.micro - key-name YOUR-KEY-PAIR

2. Cast a protective ward (security group rules) to allow Prometheus to roam free.

Step 2: Embrace Prometheus Magic
1. Enter the mystical realm (SSH into your EC2 instance):

ssh -i YOUR-KEY-PAIR.pem ec2-user@YOUR-EC2-PUBLIC-IP

2. Brew a potion (install Prometheus):

wget https://github.com/prometheus/prometheus/releases/download/v2.30.0/prometheus-2.30.0.linux-amd64.tar.gz
 tar -xvzf prometheus-2.30.0.linux-amd64.tar.gz
 cd prometheus-2.30.0.linux-amd64
 ./prometheus - config.file=prometheus.yml

5. Grafana: Painting Metrics into Enchanting Dashboards

Step 1: Raise the Grafana Banner on AWS
1. Summon another Amazon EC2 instance (like Prometheus).
2. Draw a protective circle (security group rules) to welcome Grafana.

Step 2: Enchant Your World with Grafana
1. Step into the circle (SSH into your EC2 instance).
2. Cast the spell (install Grafana):

wget https://dl.grafana.com/oss/release/grafana-8.3.0-1.x86_64.rpm
 sudo yum install grafana-8.3.0–1.x86_64.rpm
 sudo systemctl start grafana-server
 sudo systemctl enable grafana-server

6. The Enchanted Scroll: DevOps Automation with CI/CD
Make your monitoring adventure more magical by integrating Prometheus and Grafana into your DevOps pipeline. Use spells like Jenkins or AWS CodePipeline to automate deployment and configuration changes.

7. Forging Shields: AWS Security and Scalability
1. Call upon the wizardry of security: Create powerful security groups and IAM roles to protect your instances.
2. Embrace the magic of scalability: Enlist AWS auto-scaling to handle the ebb and flow of visitors to your enchanted realm.

Further Reading:

Serverless observability, monitoring, and debugging explained

OpenTelemetry: A full guide

Combining OTel and Prometheus metrics for alerting machine

Lambda monitoring: Combining the three pillars of observability to reduce MTTR