predict model and deploy model function in clustering in pycaret
This recipe explains the predict model and deploy model function in clustering in pycaret. The Predict model function predicts
Recipe Objective - What are the predict_model and deploy_model functions in the clustering model in pycaret?
PyCaret provides predict_model and deploy_model functions in the clustering module.
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Predict Model function with Example:-
PyCaret provides "pycaret.clustering.predict_model()" function. The Predict model function predicts "Label" and "Score" using a trained model.
from pycaret.datasets import get_data
jewll = get_data('jewellery')
# importing clustering module
from pycaret.clustering import *
# initialize the setup
setup_name = setup(data = jewll)
# k_means clustering
k_m = create_model('kmeans')
pred = predict_model(k_m)
pred
deploy_model function with Example:-
PyCaret provides "pycaret.clustering.deploy_model()" function. The Deploy model function deploys the transformation pipeline and trained model on cloud.
from pycaret.datasets import get_data
jewll = get_data('jewellery')
# importing clustering module
from pycaret.clustering import *
# initialize the setup
setup_name = setup(data = jewll)
# k_means clustering
k_m = create_model('kmeans')
# 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'})
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