MLOps series #2 : Deploy a Recommendation System as Hosted Interactive Web Service on AWS

ModelRegistration.py

model_name = 'Champion'
artifact_path = "model"
model_uri = "runs:/{run_id}/{artifact_path}".format(run_id=run_id, artifact_path=artifact_path)
model_details = mlflow.register_model(model_uri=model_uri, name=model_name)

client = MlflowClient()

client.update_registered_model(
  name=model_details.name,
  description="This model provides recommendation for specific users and items based on purchase data. The data consists of user transactions"
)

client.update_model_version(
  name=model_details.name,
  version=model_details.version,
  description="This model was built with Surprise library. It is a ALS based BaselineOnly algorithm"
)

client.transition_model_version_stage(
  name=model_details.name,
  version=model_details.version,
  stage='Staging',
)
model_version_details = client.get_model_version(
  name=model_details.name,
  version=model_details.version,
)
print("The current model stage is: '{stage}'".format(stage=model_version_details.current_stage))