A commercial mortgage underwriter is interested in the future prices and valuations of various commercial real estate assets. In this example, we build a machine learning pipeline that utilizes various attributes of historical real estate valuations in order to predict the future valuation of a given asset. This pipline is deployed via Kubeflow
to Vertex AI
in order to automate and scale, efficiently leveraging GCP's serverless infrastucture.
The python code for this pipeline resides in https://github.com/secrettoad/kubeflow/blob/main/multifamily_pricing/main.py
To run the pipeline, call the run_pricing_pipeline
function within main.py
To deploy a pipeline for automatic runs on new uploads to the associated gcs bucket, run the command below (you will need to authenticate to gcp first and have the appropriate permissions, as well as have created the bucket to be listened to)
gcloud functions deploy python-finalize-function \
--runtime=python38 \
--region=us-west1 \
--source=multifamily_pricing \
--entry-point=run_pricing_pipeline \
--trigger-bucket="coysu-demo-datasets" \
--memory=1024MB
The Google Cloud Functions
cli command provided at the beginning of this document creates a serverless, asynchronous function that listens for new files being added to the relevant bucket. The pipeline then uses dask
to load all relevant files within each train and test directory.
Within the function deploy_component
a model is pushed to the gcp
model registry and then exposed via an http endpoint.
Kubeflow
in conjunction with Vertex AI
is a combination of tools that enables the deployment of pipelines in a commercial-grade, production-ready manner without demanding an inordinate amount of time or resources. Contact John at [email protected] for more information.