Notebook Deployment on AWS: A Complete Guide

Are you tired of struggling with notebook deployment on AWS? Do you want to learn how to go from Jupyter notebook to model deployment in the cloud? Look no further! In this complete guide, we will walk you through the process of deploying your notebooks on AWS step-by-step.

Why Deploy Notebooks on AWS?

Before we dive into the details of notebook deployment on AWS, let's first understand why it's important. Notebooks are a powerful tool for data scientists and machine learning engineers. They allow you to experiment with different algorithms, visualize data, and collaborate with others. However, running notebooks on your local machine has its limitations. You may not have enough computing power to train large models, or you may not be able to share your work with others easily.

Deploying your notebooks on AWS solves these problems. AWS provides a scalable and reliable infrastructure that allows you to run your notebooks on powerful instances. You can also easily share your work with others by deploying your models as web services.


Before we get started, make sure you have the following:

Step 1: Create an EC2 Instance

The first step in deploying your notebooks on AWS is to create an EC2 instance. An EC2 instance is a virtual machine that runs on AWS infrastructure. You can choose the instance type based on your computing needs. For example, if you need a lot of computing power, you can choose an instance with a high number of CPUs and memory.

To create an EC2 instance, follow these steps:

  1. Go to the AWS Management Console and navigate to the EC2 service.
  2. Click on "Launch Instance" to start the instance creation wizard.
  3. Choose an Amazon Machine Image (AMI) for your instance. An AMI is a pre-configured virtual machine that includes an operating system and other software. You can choose a pre-configured AMI that includes Jupyter notebooks and other data science tools.
  4. Choose an instance type based on your computing needs.
  5. Configure the instance details, such as the number of instances, network settings, and storage.
  6. Add tags to your instance to help you identify it later.
  7. Configure security groups to control inbound and outbound traffic to your instance.
  8. Review and launch your instance.

Once your instance is launched, you can connect to it using SSH or the AWS Management Console.

Step 2: Install Jupyter Notebook

Now that you have an EC2 instance, the next step is to install Jupyter Notebook. Jupyter Notebook is a web-based interactive computing environment that allows you to create and share documents that contain live code, equations, visualizations, and narrative text.

To install Jupyter Notebook on your EC2 instance, follow these steps:

  1. Connect to your EC2 instance using SSH.
  2. Install Anaconda, which includes Jupyter Notebook, using the following command:
  1. Follow the installation wizard to install Anaconda.
  2. Start Jupyter Notebook using the following command:
jupyter notebook --ip= --no-browser

This command starts Jupyter Notebook and allows you to access it from your local machine using a web browser.

Step 3: Upload Your Notebooks to S3

Now that you have Jupyter Notebook running on your EC2 instance, the next step is to upload your notebooks to S3. S3 is a scalable and durable object storage service that allows you to store and retrieve data from anywhere on the web.

To upload your notebooks to S3, follow these steps:

  1. Create an S3 bucket in the AWS Management Console.
  2. Upload your notebooks to the S3 bucket using the AWS Management Console or the AWS CLI.
aws s3 cp my_notebook.ipynb s3://my-bucket/
  1. Make sure the S3 bucket is public or that you have the necessary permissions to access it.

Step 4: Deploy Your Model as a Web Service

The final step in notebook deployment on AWS is to deploy your model as a web service. AWS provides several services that allow you to deploy your models as web services, such as AWS Lambda, AWS Elastic Beanstalk, and AWS SageMaker.

To deploy your model as a web service, follow these steps:

  1. Choose a service that fits your needs. For example, if you want to deploy a simple model, you can use AWS Lambda. If you want to deploy a more complex model, you can use AWS SageMaker.
  2. Create a deployment package that includes your model and any dependencies.
  3. Deploy your package to the chosen service.
  4. Test your web service to make sure it's working correctly.


Deploying your notebooks on AWS is a powerful way to scale your data science work and collaborate with others. In this complete guide, we walked you through the process of notebook deployment on AWS step-by-step. We hope this guide helps you get started with notebook deployment on AWS and unlocks new possibilities for your data science work.

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