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Data science teams frequently face challenges when it comes to effectively managing their machine learning experiments. Although Jupyter notebooks are widely used, relying on them or spreadsheets to store experiment results can become overwhelming and hinder collaboration among team members. This is especially true when dealing with multiple hyperparameters, varying model versions, evolving data sources, and numerous metrics. Such an approach compromises reproducibility and makes it difficult to compare experiments. As a result, it becomes essential to find a more efficient and centralized solution for managing machine learning experiments.
MLflow addresses these challenges by offering a unified interface and a comprehensive set of tools for managing the entire machine learning lifecycle. This includes capabilities such as experiment tracking, project packaging, model versioning, and model deployment.
In this blog, we will explore the setup of MLflow using AWS services. Our focus will be on configuring MLflow to utilize Amazon RDS as the backend store for metadata and logs, Amazon S3 as the artifact location for storing models and artifacts, and an EC2 instance as the remote tracking server hosting MLflow. By hosting the tracking server remotely, data scientists can benefit from a centralized platform that allows them to store and access their own experiment results, as well as the results of their team members.
Now, let’s walk through the steps to set it up:
Log in to your AWS Management Console and navigate to the S3 service. Click on the “Create bucket” button to start creating a new bucket. In the “Bucket name” field, provide a globally unique name for your bucket.
We will proceed with the default settings and click on the “Create bucket” button without making any additional changes.
To begin launching an EC2 instance for hosting the remote tracking server, access the AWS Management Console and navigate to the EC2 service. Click on the “Launch Instances” button and assign a name to your instance
Let’s generate a new key pair to ensure secure connectivity to this instance.
Assign it a name of your preference and click on “Create new key pair”.
Locate and choose the newly created key pair from the dropdown menu. Finally, click on “Launch instance” to initiate the launch of the EC2 instance.
After the instance is created, locate the name of the VPC security group under the “Security” section. Click on the security group, and you will find an option to “Edit inbound rules.”