Using AWS Sagemaker

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SageMaker notebook instances enable you to concentrate solely on machine learning while keeping your compute environment secure and up to date with the latest open-source software. You can simplify your data workflows by using a unified notebook environment for data engineering, analytics, and machine learning.

DeepSense AWS SageMaker Notebook Instances

You can find the Notebook instances types in Resources DeepSense has aquired Amazon SageMaker notebook instances to give users access to use the notebook instance if they develop the machine learning in notebook. Amazon SageMaker notebook instances is a machine learning (ML) compute instance running the Jupyter Notebook App. SageMaker manages creating the instance and related resources. Use Jupyter notebooks in your notebook instance to prepare and process data, write code to train models, deploy models to SageMaker hosting, and test or validate your models. A machine learning (ML) compute instance running the Jupyter Notebook App on AWS SageMaker. SageMaker is in charge of creating the instance and its associated resources. Prepare and process data in Jupyter notebooks in your notebook instance, write code to train models, deploy models to SageMaker hosting, and test or validate your models.

AWS administrator will create DeepSense AWS IAM user account. AWS will send you an email containing information about your account. and users will have access to the AWS SageMaker Notebook instance.

Accessing your DeepSense AWS SageMaker Notebook Instance

  • Access your AWS console (from email – you will be asked to create new password)
  • Once everything is in place, you will see the AWS console dashboard.
  • Navigate to security credentials in the right top corner, where the account name is written.
  • Follow the on-screen instructions to enable MFA. (
  • Begin with SageMaker Notebook.
  • Enter SageMaker into the search bar and launch the service.
  • From the console's left side panel, select Notebook instance.
  • Check the region set to “Canada (Central)” by going on top right corner beside your account name.
  • Look for a notebook instance called "ProjectName-DS-notebook-ca-d-instancetype"
  • Select the instance, then go to the Actions drop down menu and choose Start.
  • In a few moments, the notebook's status will change to starting and it will boot up.
  • After notebook is in running status, you can select the option to Open it in Jupyter.
  • The notebook will be ready to use, just like the Jupyter on localhost.
  • In addition, there is JupyterLab also accessible for expandible use.
  • When you're finished with your work, log out of Jupyter.
  • Stop the instance from the same menu where you started it.

It is necessary to turn off the notebook instance so that we are not charged for unutilized hours. So, when you're finished, please stop the instance.