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3. Open the **Amazon S3** console at https://console.aws.amazon.com/s3 or choose the Amazon S3 service in the menu.
4. In the Amazon S3 console, click the **Create Bucket** button.
-5. For the **Bucket Name**, type _smlambda-workshop-**[your-initials]**_ in the text box and click Next (take note of the bucket name, it will be needed later for loading data in the notebook instance). Leave everything default in the next 2 pages and click **Create Bucket** in the Review page.
+5. For the **Bucket Name**, type _smlambda-workshop-**[your-initials]**_ in the text box and click Next (take note of the bucket name, it will be needed later for loading data in the notebook instance). Press **Next** to move to the next screen.
Note: if the bucket name is already taken, feel free to add an extra suffix.
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+6. Enable versioning of the objects in the bucket as shown in the screen below. This will be required to use AWS CodePipeline in the next sections of the workshop.
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+ Press **Next** and then **Next** again leaving the settings as they are in the following screen.
+7. Finally, click **Create Bucket** in the Review page.
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### Create a managed Jupyter Notebook instance
In this section we will use an Amazon SageMaker managed Jupyter notebook instance to prepare and process data and write the code to train the model.