[Bug Report] machine_learning_workflow_abalone has wrong S3 bucket path structure

**Link to the notebook**
https://github.com/aws/amazon-sagemaker-examples/blob/main/step-functions-data-science-sdk/machine_learning_workflow_abalone/machine_learning_workflow_abalone.ipynb

**Describe the bug**
The bucket path variable is set the S3 URL when it should be the S3 URI. The training step estimator uses this variable for the output path. This fails the SageMaker training job with BucketRegionError.

**To reproduce**
Reproduced by running the notebook as is. The fix tested on my end would be to change `bucket_path = "https://s3-{}.amazonaws.com/{}".format(region, bucket)` to `bucket_path = "s3://{}".format(bucket)` so that `bucket_path` is an S3 URI.

**Logs**
Error message:

ClientError: Artifact upload failed:Unable to upload file to s3://s3-us-west-2.amazonaws.com//sagemaker/DEMO-xgboost-regression/single-xgboost/regression-xxxx/debug-output/events/000000000030/000000000030_worker_0.tfevents (BucketRegionError: bucket must be in the same region as the job)

Open step-functions-data-science-sdk/machine_learning_workflow_abalone/machine_learning_workflow_abalone.ipynb and inspect the bucket_path value used by the training estimator's output path. Run the notebook or its training step to reproduce the BucketRegionError; done means the job accepts the output location without that error.

Written by the indexing model from the issue text.