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Detect data drift in NLP using MMD
Issue #, if available:
Description of changes: This notebook demonstrates using Amazon SageMaker Model Monitor to detect data drift in NLP using Maximum Mean Discrepancy (MMD) distance measure
Testing done: Yes
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Put an x in the boxes that apply. You can also fill these out after creating the PR. If you're unsure about any of them, don't hesitate to ask. We're here to help! This is simply a reminder of what we are going to look for before merging your pull request.
- [x] I have read the CONTRIBUTING doc and adhered to the example notebook best practices
- [ x] I have updated any necessary documentation, including READMEs
- [x] I have tested my notebook(s) and ensured it runs end-to-end
- [x ] I have linted my notebook(s) and code using
tox -e black-format,black-nb-format
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The embeddings.npy file is 19 MB - let's host in S3 rather than committing to the repo.
I see a lot of duplicated files (4+) in this PR between the two notebooks - what if they are in the same directory to reuse files, rather than having duplicates that could drift apart over time?
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Run black-nb -l 100 {notebook}.ipynb for each notebook to apply code formatting.
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