Here I have made an end to end scikit learn machine learning pipeline and then deployed it on google cloud using following steps:

1)Install xgboost using pip install xgboost and give the service account necessary permissions.

2)Create a notebook instance named 'model-demo' in GCP and create a bucket named 'churn-model-pra' to store the dataset for training the model.

3)Create a 'telco_churn.ipynb' notebook that includes the pipeline used to predict whether a customer will churn or not.

4)Save the model in a 'model.joblib' file using the joblib.dump(pipeline_xgb, 'model.joblib') command.

5)Copy the 'model.joblib' file to the bucket using the command !gsutil cp ./model.joblib gs://$BUCKET_NAME/model.joblib.

6)Create a file named 'predict_setup.ipynb' to write two files: 'predictor.py' and 'setup.py'. 'predictor.py' contains the 'ChurnPredictor' class and setup.py contains the dependencies and their versions. Convert the setup.py file into a '.tar' file using !python setup.py sdist --formats=gztar.

7)Copy the 'custom_predict-0.1.tar.gz' file to the 'churn-model-pra' bucket using the command !gsutil cp ./dist/custom_predict-0.1.tar.gz gs://churn-model-pra/custom_predict-0.1.tar.gz.

8)Create the model using the command !gcloud beta ai-platform models create ChurnPredictor5 --regions us-central1 --enable-console-logging.

9)Create the version of the model using the command !gcloud --quiet beta ai-platform versions create V1 --model ChurnPredictor5 --runtime-version 2.9 --python-version 3.7 --origin gs://churn-model-pra/ --package-uris gs://churn-model-pra/custom_predict-0.1.tar.gz --prediction-class predictor.ChurnPredictor