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.
Accountfield enter your 12-digit AWS account ID. You can find it on the My Account page.
SageMakerNotebookproduct and click
team-<PRODUCT NAME>or similar
Nextand then click
You will land at a Provisioned Product page while the Service Catalog creates your Jupyter Notebook. Periodically click Refresh until the Status reads
Succeeded. This should table about 5 minutes to launch your 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.
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.
Read through the notebook and in the next lab we will execute the notebook cells to exercise the security controls of our environment.