Build, train and deploy a machine learning model using Amazon Sagemaker

  • Taking machine learning models from concept to production is often difficult and time-consuming.
  • To train the model, one must first manage enormous amounts of data, then determine the optimal technique for training it.
  • After that, one must manage computing capacity while training it, and then deploy it into a production environment.
  • Amazon SageMaker simplifies the process of creating and deploying machine learning models.
  • SageMaker controls all of the underlying infrastructures to train your model at petabyte size and deploy it to production.
  • Here, in this article, we will learn more about Amazon Sagemaker.
  • We will learn how to create a SageMaker notebook instance.
  • Next, we will see how to prepare the data.
  • Then, we will train the model to learn from the data.
  • At last, we will Deploy our machine learning model.
  • The Bank Marketing Data Set will be used to train the model.

Steps to Build, train and Deploy a Machine Learning model into Amazon SageMaker

1. For data preparation, create an Amazon SageMaker notebook instance:

  • You build the notebook instance in this stage, which you’ll use to download and process your data.
  • You also build an Identity and Access Management (IAM) role that allows Amazon SageMaker to access data in Amazon S3 as part of the setup procedure.
  • Sign in to the Amazon SageMaker interface and choose your desired AWS Region in the top right corner.
  • Choose Notebook instances from the left navigation window, then Create notebook instance.
  • Fill in the following fields in the Notebook instance setting box on the Create notebook instance page:
  • Type <Name of the notebook> in the Notebook instance name field.
  • Choose ml.t2.medium as the Notebook instance type.
  • Keep the default selection -> none for elastic inference.
  • Choose to Create a new role in the Permissions and encryption section for the IAM role.
  • Then in the Create an IAM role dialogue box, pick Any S3 bucket and Create role.

2. Data Preparation:

  • Choose Open Jupyter after the status of your SageMaker notebook instance changes to InService.
  • Choose New in Jupyter, and then conda python3.
  • Copy and paste the following code into a new code cell in your Jupyter notebook, then pick Run.
  • To save your data, create an S3 bucket.
  • Choose Run after copying and pasting the following code into the next code box.
  • Load the data into a dataframe after downloading it to your SageMaker instance.
  • Choose Run after copying and pasting the above code into the next code box.
  • After that one can perform train test split with the help of the following command.

3. Train the ML Model:

  • Copy and paste the following code into a new code cell in your Jupyter notebook, then pick Run.
  • Set up the Amazon SageMaker session, create an instance of the XGBoost model (an estimator), and define the model’s hyperparameters.
  • Copy and paste the above code into the next code cell and choose Run.
  • Start the training job.

4. Model Deployment:

  • Copy and paste the above code into a new code cell in your Jupyter notebook, then pick Run.
  • Copy the following code into the next code box and pick Run to forecast whether clients in the test data enrolled for the bank product or not.

Conclusion:

  • So far in this article, we covered a high-level overview of how to train, test and deploy a machine learning model using Amazon Sagemaker.
  • In the next article, we will learn about the basics of Amazon Augmented AI.

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