predict and deploy model function in anomaly detection pycaret

Recipe Objective - What are the predict_model and deploy_model functions in anomaly detection in pycaret?

PyCaret provides predict_model and deploy_model functions in the anomaly detection module.

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Predict Model function with Example:-

PyCaret provides "pycaret.anomaly.predict_model()" function. The Predict model function generates anomaly labels on using a trained model.

from pycaret.datasets import get_data
anmly = get_data('anomaly')
# importing anomaly module
from pycaret.anomaly import *
# initialize the setup
setup_name = setup(data = anmly)
# Knn model
knn = create_model('knn')
# predict model function
knn_predictions = predict_model(model = knn, data = anmly)


deploy_model function with Example:-

PyCaret provides "pycaret.anomaly.deploy_model()" function. The Deploy model function deploys the transformation pipeline and trained model on cloud.

from pycaret.datasets import get_data
anmly = get_data('anomaly')
# importing anomaly module
from pycaret.anomaly import *
# initialize the setup
setup_name = setup(data = anmly)
# Knn model
knn = create_model('knn')
# sets appropriate credentials for the platform as environment variables
import os
# set your cloud ACCESS_KEY_ID, SECRET_ACCESS_KEY
os.environ["AWS_ACCESS_KEY_ID"] = str("foo")
os.environ["AWS_SECRET_ACCESS_KEY"] = str("bar")
deploy_model(model = lr, model_name = 'lr-for-deployment', platform = 'aws',
    authentication = {'bucket' : 'S3-bucket-name'})