While I'm unable to access real-time data or perform live comparisons, but I can provide a general overview of the pricing structures of AWS SageMaker and Azure Machine Learning as of my last knowledge update. Keep in mind that these details may have changed since then, so you should consult the respective websites for the most up-to-date information.

AWS SageMaker Pricing:

1. Notebook Instances: Pay-as-you-go pricing based on the instance type you choose (from t3.medium to ml.m5.24xlarge).

2. Training: Billed per second, based on the training instance type you choose. There are various types available, such as general purpose, compute-optimized, memory-optimized, GPU instances, etc.

3. Deployment: Billed per second, based on the instance type used for hosting the model. There are similar instance types as the training instances.

4. Data Processing and Storage: Additional charges for data storage and data processing using Amazon S3, Amazon EFS, and Amazon FSx.

Azure Machine Learning Pricing:

1. Compute Instances: Pay-as-you-go pricing based on the virtual machine type you choose (from Standard_D1_v2 to Standard_M128ms).

2. Training: Billed per minute, based on the training instance type you choose. There are various types available, such as general purpose, compute-optimized, memory-optimized, GPU instances, etc.

3. Deployment: Billed per minute, based on the instance type used for hosting the model. There are similar instance types as the training instances.

4. Data Storage: Additional charges for data storage using Azure Blob Storage, Azure File Storage, and Azure Data Lake Storage.

Remember that the cost of using these services is influenced by various factors such as data storage, data transfer, and additional services used in combination with machine learning platforms. To get accurate cost estimates for your specific use case, you should consult the respective pricing calculators provided by AWS and Azure.
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