Using AWS SageMaker Studio

Amazon SageMaker Studio is a web-based integrated development environment (IDE) for machine learning (ML) that allows data scientists and developers to perform all ML development steps, from data preparation to model building, training, deployment, and management, on a single, unified platform.

DeepSense AWS SageMaker Studio

You can find the Studio domain from SageMaker service dashboard's left side panel. These are the instances Resources which can be used. DeepSense has acquired Amazon SageMaker Studio Domains per project to give users access to use the Studio IDE. The Studio also includes a comprehensive set of tools for every stage of machine learning development, from data preparation to building, training, deploying, and managing ML models. It enables users to jump between these steps quickly to fine-tune their models, replay training experiments, and scale to distributed training directly from JupyterLab, Code Editor, or RStudio. . Use Studio notebooks in your project domain to prepare and process data, 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 Studio specific to thee project in new domain.

Accessing DeepSense AWS SageMaker Studio

• Accept the invitation sent in the welcome email and navigate to the AWS Management Console login page.
• Alternatively, use the URL provided in the AWS correspondence email to sign in.
• Enter the username as specified in the email.
• On your first login, you will be prompted to create a password.
• After creating the password, log in again using your username and the newly created password.
• During the first login, you will be redirected to enable Multi-Factor Authentication (MFA). Follow the on-screen instructions to complete the MFA setup.
• Once MFA is set up, you will be directed to the AWS Access Portal.
• Click on the Applications tab and select the assigned SageMaker Studio. This will redirect you to your Studio IDE.

Working on DeepSense AWS SageMaker Studio

• From your Studio UI, start by creating a JupyterLab space. Click on JupyterLab and use the Create a new space button located in the top-right corner.
• If you have a supervisor, select Shared space; otherwise, create a Private space.
• After creating the space, select it and review the details.
• To open the Jupyter application, select the JupyterServer application and launch the specific instance type you are instructed to use.
• (Optional) In the Change environment dialog box, you can select a start-up script from the dropdown menu if needed.
• Use the ml.t3.medium instance type if you are editing code without executing it.
• After editing your code, save your changes. Stop the current instance, select the required instance type from the dropdown menu, and restart it.
• To create a new notebook in JupyterLab: go to File, select New, and then Notebook. A new notebook tab will open in a new window.
• To stop an instance after completing your work, navigate to the Running instances section in the left pane of the SageMaker console.
• Click the Stop button under the Actions column for the notebook instance you wish to stop.
• Once the notebook instance is stopped, you can restart it anytime by clicking the Start button.

It is necessary to stop the notebook instance in order to avoid being charged for not utilized hours. So, when you're finished, please stop the instance.