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Fix 'JSONLines' -> 'JSON Lines' #3555

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merged 5 commits into from
Aug 18, 2022
Merged

Fix 'JSONLines' -> 'JSON Lines' #3555

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77d6b04
Fix 'JSONLines' -> 'JSON Lines'
jkroll-aws Aug 17, 2022
6b93dc0
Merge branch 'main' into jsonlines3
atqy Aug 18, 2022
1412fe4
Merge branch 'main' into jsonlines3
atqy Aug 18, 2022
93752da
Merge branch 'main' into jsonlines3
atqy Aug 18, 2022
5ebe67c
Merge branch 'main' into jsonlines3
atqy Aug 18, 2022
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2 changes: 1 addition & 1 deletion sagemaker_processing/fairness_and_explainability/fairness_and_explainability.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -50,7 +50,7 @@
"1. Explaining the importance of the various input features on the model's decision\n",
"1. Accessing the reports through SageMaker Studio if you have an instance set up.\n",
"\n",
"In doing so, the notebook first trains a [SageMaker XGBoost](https://docs.aws.amazon.com/sagemaker/latest/dg/xgboost.html) model using training dataset, then use SageMaker Clarify to analyze a testing dataset in CSV format. SageMaker Clarify also supports analyzing dataset in [SageMaker JSONLines dense format](https://docs.aws.amazon.com/sagemaker/latest/dg/cdf-inference.html#common-in-formats), which is illustrated in [another notebook](https://github.com/aws/amazon-sagemaker-examples/blob/master/sagemaker_processing/fairness_and_explainability/fairness_and_explainability_jsonlines_format.ipynb)."
"In doing so, the notebook first trains a [SageMaker XGBoost](https://docs.aws.amazon.com/sagemaker/latest/dg/xgboost.html) model using training dataset, then use SageMaker Clarify to analyze a testing dataset in CSV format. SageMaker Clarify also supports analyzing dataset in [SageMaker JSON Lines dense format](https://docs.aws.amazon.com/sagemaker/latest/dg/cdf-inference.html#common-in-formats), which is illustrated in [another notebook](https://github.com/aws/amazon-sagemaker-examples/blob/master/sagemaker_processing/fairness_and_explainability/fairness_and_explainability_jsonlines_format.ipynb)."
]
},
{
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2 changes: 1 addition & 1 deletion sagemaker_processing/fairness_and_explainability/fairness_and_explainability_outputs.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -70,7 +70,7 @@
"1. Explaining the importance of the various input features on the model's decision\n",
"1. Accessing the reports through SageMaker Studio if you have an instance set up.\n",
"\n",
"In doing so, the notebook first trains a [SageMaker XGBoost](https://docs.aws.amazon.com/sagemaker/latest/dg/xgboost.html) model using training dataset, then use SageMaker Clarify to analyze a testing dataset in CSV format. SageMaker Clarify also supports analyzing dataset in [SageMaker JSONLines dense format](https://docs.aws.amazon.com/sagemaker/latest/dg/cdf-inference.html#common-in-formats), which is illustrated in [another notebook](https://github.com/aws/amazon-sagemaker-examples/blob/master/sagemaker_processing/fairness_and_explainability/fairness_and_explainability_jsonlines_format.ipynb)."
"In doing so, the notebook first trains a [SageMaker XGBoost](https://docs.aws.amazon.com/sagemaker/latest/dg/xgboost.html) model using training dataset, then use SageMaker Clarify to analyze a testing dataset in CSV format. SageMaker Clarify also supports analyzing dataset in [SageMaker JSON Lines dense format](https://docs.aws.amazon.com/sagemaker/latest/dg/cdf-inference.html#common-in-formats), which is illustrated in [another notebook](https://github.com/aws/amazon-sagemaker-examples/blob/master/sagemaker_processing/fairness_and_explainability/fairness_and_explainability_jsonlines_format.ipynb)."
]
},
{
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