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WHAT

  1. Renamed the over long "getting started with..." to "ml-function-classify" - easier to read
  2. Add a new notebook that demonstrates how you can use ML Function Anomaly Detect
  3. Add a notebook template which show how you can deploy a Python UDF that takes image urls and performs operation on the image present at the URL

WHY

Need to demo the possibilities to Sales / Solution engineers
Built on top of branch of #149

Comment on lines +370 to +371
"selected_features: {\"mode\":\"*\",\"features\":null}\n",
"force: True"
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@bharathts07 bharathts07 Oct 31, 2025

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@syutika added this force command based on you suggestion

"source": [
"# Display the training result\n",
"training_result"
"print(json.dumps(training_result, indent=4))"
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For better indentation in output

"metadata": {},
"outputs": [],
"source": [
"import json\n",
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import needed for indenting json output in notebook

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