How To Setup MLflow on an AWS EC2 Instance for Tracking your ML Experiments

This will be one of my first blog series on MLOps on the Cloud. In this post i’l explain in steps how you can setup MLFlow on an AWS EC2 instance and use AWS S3 bucket to store your experiment artifacts For many MLOps engineers, choosing the right MLOps platform, whether on-prem or in the cloud, poses a constant dilemma. Personally, I advocate for the cloud. One key advantage of a robust ML platform is its ability to leverage the cloud effectively. Cloud flexibility adapts to changing hardware requirements, providing instant access to the latest resources and features. The main goal is to choose a ML platform that simplifies tasks for data scientists, by giving them the capability of tracking and recording experiments, which can then be shared and compared. Additionally, it encompasses the easy storage , management and deployment of machine learning models and there artifacts. The post is sectioned into 4 steps as listed below <ol> <li>Log in to your AWS console and create an IAM user with AdministratorAccess.</li> <li>Generate IAM user Accesskeys and Export the credentials to your AWS CLI</li> <li>Create an S3 bucket to store your experiment artifacts.</li> <li>Create an EC2 instance (Ubuntu) and configure Security Groups to allow traffic on port 5000.</li> </ol> <a href="https://medium.com/@oyelamifiyin/how-to-setup-mlflow-on-an-aws-ec2-instance-for-tracking-you-ml-experiments-d2472cadd784">Click Here</a>