Hi,
I am aware that we can set default artifact root option to S3 bucket in mlflow server
mlflow server
--backend-store-uri /mnt/persistent-disk
--default-artifact-root s3://my-mlflow-bucket/
--host 0.0.0.0
By doing this, we are setting default storage for artifacts to S3 buckets for all runs.
As a data scientist, I would be training model multiple times and then comparing using MLFlow Tracking UI. I would always suggest to store artifacts at local file system and only push best model to S3 bucket. Basically I am implementing CI/CD pipeline for MLFlow -sagemaker. As soon as best model pushed to S3 bucket, i would trigger CI/CD pipeline and deploy the model to AWS sagemaker endpoint and then using API Gateway i would create the REST API.
Do we have any such MLFlow API which can be used to push the model only for particular run_id ?
From documentation, i couldn't find any such API.
Regards,
Vackysh
Hi,
I am aware that we can set default artifact root option to S3 bucket in mlflow server
mlflow server
--backend-store-uri /mnt/persistent-disk
--default-artifact-root s3://my-mlflow-bucket/
--host 0.0.0.0
By doing this, we are setting default storage for artifacts to S3 buckets for all runs.
As a data scientist, I would be training model multiple times and then comparing using MLFlow Tracking UI. I would always suggest to store artifacts at local file system and only push best model to S3 bucket. Basically I am implementing CI/CD pipeline for MLFlow -sagemaker. As soon as best model pushed to S3 bucket, i would trigger CI/CD pipeline and deploy the model to AWS sagemaker endpoint and then using API Gateway i would create the REST API.
Do we have any such MLFlow API which can be used to push the model only for particular run_id ?
From documentation, i couldn't find any such API.
Regards,
Vackysh