Moving MLflow to the cloud involves configuring remote artifact storage and securing the tracking server.
1. Using S3 as an Artifact Store¶
Set your environment variables and point the tracking server to an S3 bucket.
set AWS_ACCESS_KEY_ID=...
set AWS_SECRET_ACCESS_KEY=...
mlflow server --default-artifact-root s3://my-mlflow-bucket/ ...2. Authentication and RBAC¶
MLflow supports basic authentication. When enabled, users must log in to view experiments or register models.
mlflow server --app-name basic-auth