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articles/machine-learning/concept-automated-ml.md

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@@ -195,18 +195,18 @@ The following table shows a summary of features available on local and remote co
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| DNN-BERT-based text featurization and training || ||
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| Out-of-the-box GPU support (training and inference) || ||
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| Image Classification and Labeling support || ||
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| Feature engineering customization UI || | |
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| Auto-ARIMA, Prophet and ForecastTCN models for forecasting || ||
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| Multiple runs/iterations in parallel || ||
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| Create and run experiments in studio web experience || ||
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| Feature engineering customization UI || | |
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| Azure ML hyperparameter tuning || | |
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| Azure ML Pipeline workflow support || | |
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| Continue a run || | |
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| Create and run experiments in studio web experience || ||
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| Forecasting ||||
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| Model interpretability (in notebooks) ||||
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| Create and run experiments in notebooks ||| |
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| Register and visualize experiment's info and metrics in UI ||| |
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| Data guardrails ||| |
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| Forecasting ||||
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| Model interpretability (in notebooks) ||||
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## Automated ML in Azure Machine Learning

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