Lab 3: Deploy a Jupyter Notebook

At this point, the cloud platform engineering team has built a secure base environment to host data analytics environments. The data science administrator has provisioned resources within that environment for your team to work, and they have provided you with an AWS Service Catalog to enable you to provision SageMaker notebooks. Now, use these resources to provision a Jupyter notebook and start developing your ML solution.

Launch the notebook product

Assume the role of the Data Scientist and visit the Service Catalog product listing. Launch a SageMakerNotebook product using the Team Name defined by the Data Science Administrator.

Step-by-step instructions

Provisioned Notebook Product

Access the notebook

After the notebook has launched successfully you can open it by visiting the SageMaker console. With your Jupyter notebook open, familiarize yourself with the web interface and open the Notebook kernel named stockmarket_predictor_v5.ipynb. Don’t forget to reference back to the Jupyter cheat sheet for a quick reference.

Step-by-step instructions

When its open the Notebook kernel should use the conda_tensorflow_p27 kernel. If Jupyter asks you to set the kernel select conda_tensorflow_p27 and if Jupyter displays an error, reload the Jupyter page by clicking your browser Refresh button.

Jupyter Notebook Interface

Read through the notebook and in the next lab we will execute the notebook cells to exercise the security controls of our environment.