Test Scalable ML notebooks on unstable

[cbur24]

Proposed changes

The 4_Classify_satellite_data notebook was failing on the unstable image. This is occuring because the setting predict_xr(..., persist=True) resulted in the predictions occassionally hanging, with no warnings or errors. To pass the tests I have removed the persist=True flag from the predictions (wasn't really necesary anyway). It will be a longer piece of work to identify why persist=True intermittently fails.

Checklist

If this is a notebook, then have you:

• Checked the structure of the notebook follows our DEA-notebooks template
• Removed any unused Python packages from Load packages
• [x ] Removed any unused/empty code cells
• Removed any guidance cells (e.g. General advice)
• Ensured that all code cells follow the PEP8 standard for code. The jupyterlab_code_formatter tool can be used to format code cells to a consistent style: select each code cell, then click Edit and then one of the Apply X Formatter options (YAPF or Black are recommended).
• Included relevant tags in the final notebook cell (refer to the DEA Tags Index, and re-use tags if possible)
• Tested notebook on the DEA Sandbox
• Cleared all outputs, run notebook from start to finish, and save the notebook in the state where all cells have been sequentially evaluated
• If applicable, update the Notebook currently compatible with line below the notebook title to reflect the environments the notebook is compatible with
• Check for any spelling mistakes using the DEA Sandbox's built-in spellchecker (double click on markdown cells then right-click on pink highlighted words). For example:

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[robbibt]

Line #2.    input_data_sample = input_data.sample(150).reset_index(drop=True)

@cbur24 This uses 150 but the comment says 100

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[GL-S]

Had a look to the changes, it's all in order! There's just that little typo already mentioned by Robbi

[robbibt]

Looks great - I fixed the minor comment issue

[cbur24]

Thanks for reviewing and merging @robbibt @GL-S