Deployment

Build, train and deploy a churn prediction model using Amazon SageMaker

  • login to the S3 Console
  • create an S3 bucket where model and training data will be stored.
  • login to the SageMaker Console
  • click on ‘notebook instances’ and create a notebook instance
  • enter a name for your notebook instance
  • select ml.m5.xlarge as your notebook instance type
  • under ‘IAM role’ create a new role
  • select ‘Any S3 bucket’ and click create new role image
  • go ahead and click on ‘Create notebook instance’ image
  • under ‘notebook instance’ once your instance status will show ‘InService’ , click on ‘OpenJupyter’ under Actions image
  • click on the ‘SageMaker Examples’ tab
  • click on ‘Introduction to Applying Machine Learning’
  • click on the’use’ button next to xgboost_customer_churn.ipynb image
  • Create a copy in your home directory
  • select Conda_python3 as your kernel
  • go through the SageMaker notebook
  • point to your S3 bucket where the sample dataset will be downloaded to. image

  • change Cell 6 (shown in figure) to the following snippet: cell6

    !wget https://bit.ly/ako2020-churn-dataset
!unzip -o ako2020-churn-dataset

  • you can now click on the ‘Cell’ tab, as shown in figure. Alternatively, you can select a cell and hit SHIFT+Enter on your keyboard to evaluate a single cell. image

  • Run up to Step 17, as shown in figure last step

  • Make sure you don’t run the optional cleanup yet cleanup

Go through the notebook and examine the different steps:

  1. Data Exploration
  2. Model Training
  3. Host

Once the model is deployed and an endpoint created, we can move to the next stage.