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SageMaker Notebooks now support diffing

Source: AWS You can now compare and view differences between two notebooks that are version controlled with Git in SageMaker. Jupyter notebooks (which SageMaker notebooks are built upon) are stored as JSON documents which include tags and metadata, making it difficult to diff with traditional line-by-line text diffing tools.  

Amazon SageMaker now supports accelerated training with new, smaller, Amazon FSx for Lustre file systems

Source: AWS Amazon SageMaker customers can now use smaller Amazon FSx for Lustre file systems as the data source for training machine learning models. Until today, the smallest FSx for Lustre file system that could be created was 3.6 TBs. For training sets that are smaller than this size, customers can now create and use file systems as small as 1.2 TB.

SageMaker Batch Transform now enables associating prediction results with input attributes

Source: AWS Amazon SageMaker Batch Transform enables you to run predictions on datasets stored in Amazon S3. It is ideal for scenarios where you are working with large batches of data and don’t need sub-second latency. You can now configure your Batch Transform Jobs to exclude certain data attributes from prediction requests, and to join some or all of the input data attributes with prediction results. As a result, you no longer need additional pre-processing or post-processing when running batch predictions on data that is in CSV or JSON format.