In this section, you will learn how to use Amazon SageMaker to test and deploy our Rekognition model. Amazon SageMaker is a modular, fully managed machine learning service that enables developers and data scientists to build, train, and deploy ML models at scale.
The main interface for Amazon SageMaker projects is through Jupyter notebooks. Jupyter is an interactive Python environment designed for rapid iteration. Amazon SageMaker makes deploying and managing Jupyter notebooks easy.
From your Amazon SageMaker console, select Notebook instances then Create notebook instance.
Enter a name, such as analogue-gauge-detection, for your notebook instance, leave everything else in this section as the default.
A SageMaker Execution Role has already been created. Select the existing AmazonSageMaker-ExecutionRole- prefixed name role from the pull-down list. The exact role title may differ from that shown below.
Only if no existing AmazonSageMaker-ExecutionRole- prefixed name role exists in the AWS account you will need to select Create a new role from the selection list. If being used in a lab environment, on the pop-up menu select Any S3 bucket to allow the notebook instance to any S3 buckets in your account. Then, click on Create role button on the bottom.
Leave the remaining sections as default and click Create notebook instance.
In the Amazon SageMaker console, select Notebook instances and click on the notebook title you created in the previous step. This will open the control panel to the instance itself.
The notebook instance will take a few minutes to initialise. Wait until the instance status moves from Pending to InService on the Amazon SageMaker Notebook Instance console.
Once the notebook status is InService, open the managed Jupyter notebook by clicking on Open Jupyter.
You should see the Jupyter notebook console shown below.
In the next step you will upload and modify a Jupyter notebook.