AWS SageMaker: Sign In, Set up SageMaker Studio and Use Jupyter Notebook Instance

Sign Up for AWS Account

First, head on over to AWS and create an account. Note the following points:

• You have to give your Credit Card Information
• You might be charged depending on your usage
• There is an option for a free tier, but it keeps changing

Sign up for an AWS Free Tier Account here: Link

Once you sign up for your AWS Account, you will see your Amazon Console Home.

Photo by ANIRUDH on Unsplash

Create S3 Bucket for storage

Search for S3 in the Search Bar

Select create a bucket.

Give a name and select the below options.

Great, you have now successfully created your S3 bucket.

Keep the rest of the settings as default and create your S3 bucket!

Create ECR

Here, we will create a repository.

Select Create a repository and enter a unique name.

You can get your ARN by simply copying the URI below. You will need this to setup Sagemaker in the next step.

Set up SageMaker on AWS

You Home Console will look something like this:

Here, search for Sagemaker in the search bar and select the first option.

Once you are on the Amazon SageMaker page, select the “Getting Started” from the left pane.

Create a Role

Select Create a Role and enter the information for your desired role.

Configure ML Activities

Click Next and select all the options as mentioned below. Don’t select more than 10 roles — AWS does not allow it.

Add the bucket name you created in all of the configuration fields. First, enter S3 bucket name.

Next, enter the S3 bucket again and the URI that you copied. We have to convert the URI that we have into ARN format. Here is mine:

arn:aws:ecr:ap-southeast-2:038365619140:repository/mlopsanish

Next, enter your S3 bucket again and primary for the default workgroup.

Click the next button.

Add Additional policies & tags

Review Role

Validate

Search for IAM, and select Roles.

You will see your role show up here, with the prefix SageMaker.

Create Jupyter Notebook Instance

SageMaker Domain

Next, go back to SageMaker and configure a SageMaker Domain.

Do a quick setup.

It will work on creating a domain.

Once it is set, you will be able to access AWS SageMake Studio

Create Jupyter Notebook Instance on AWS SageMaker

Search for SageMaker Studio in AWS Home Console.

Go to Notebook Instances on the left panel as shown.

Select Create Notebook Instance

Create a notebook instance and hit the orange create

You will see the notebook being created.

Once it is set up, you will see the Create Notebook instance being “InService”

Select your notebook and then select Open JupyterLab

Select “Open Jupyter” and Tada! In a few minutes, you will have Jupyter Notebook hosted on the cloud.

Select conda3_python as the interpreter of choice and run!