AWS solutions documentation change
Summary
Fixed formatting issues in documentation, including quotation marks, JSON formatting, and whitespace. Updated examples and parameter descriptions for clarity.
Security assessment
The changes are purely formatting and clarity improvements with no indication of addressing security vulnerabilities or adding security-related content.
Diff
diff --git a/solutions/latest/mlops-workload-orchestrator/template-option-1-single-account-deployment-2.md index 2318161cb..a32a86167 100644 --- a/solutions/latest/mlops-workload-orchestrator/template-option-1-single-account-deployment-2.md +++ b/solutions/latest/mlops-workload-orchestrator/template-option-1-single-account-deployment-2.md @@ -29 +29 @@ The MLOps Workload Orchestrator solution’s AWS API Gateway has two main API en - * `model_name`: Arbitrary model name for the deploying model. The solution uses this parameter to create an Amazon SageMaker AI model, endpoint configuration, and endpoint with extensions on model name, such as ``<model_name>`-endpoint-config` and ``<model_name>`-endpoint`. The `model_name` is also used in the name of the deployed AWS CloudFormation stack for all pipelines. + * `model_name`: Arbitrary model name for the deploying model. The solution uses this parameter to create an Amazon SageMaker AI model, endpoint configuration, and endpoint with extensions on model name, such as `<model_name>`-endpoint-config` and `<model_name>`-endpoint`. The `model_name` is also used in the name of the deployed AWS CloudFormation stack for all pipelines. @@ -33 +33 @@ The MLOps Workload Orchestrator solution’s AWS API Gateway has two main API en - * `model_package_name`: Amazon SageMaker AI model package name (for example, `"arn:aws:sagemaker:`<region>`:`<account_id>`:model-package/`<model_package_group_name>`/`<model_version>`"`). + * `model_package_name`: Amazon SageMaker AI model package name (for example, `"arn:aws:sagemaker:[.replaceable]`<region>`:[.replaceable]`<account_id>`:model-package/`<model_package_group_name>`/`<model_version>`"`). @@ -35 +35 @@ The MLOps Workload Orchestrator solution’s AWS API Gateway has two main API en - * `baseline_data`: Path to a csv file in S3 assets bucket containing the data with features names used for training the model (for data quality, model bias, and model explainability monitors), or model predictions and ground truth labels (for model quality monitor), for example a csv file with the header `“prediction, probability, label”` for a `BinaryClassification` problem. + * `baseline_data`: Path to a csv file in S3 assets bucket containing the data with features names used for training the model (for data quality, model bias, and model explainability monitors), or model predictions and ground truth labels (for model quality monitor), for example a csv file with the header ` "prediction, probability, label" ` for a `BinaryClassification` problem. @@ -39 +39 @@ The MLOps Workload Orchestrator solution’s AWS API Gateway has two main API en - * `data_capture_location`: Path to a prefix in an S3 Bucket (including the bucket’s name, for example ``<bucket-name>`/`<prefix>``) to store the data captured by the real-time Amazon SageMaker AI inference endpoint. + * `data_capture_location`: Path to a prefix in an S3 Bucket (including the bucket’s name, for example `<bucket-name>`/`<prefix>`) to store the data captured by the real-time Amazon SageMaker AI inference endpoint. @@ -41 +41 @@ The MLOps Workload Orchestrator solution’s AWS API Gateway has two main API en - * `batch_inference_data`: Path to a file in an S3 Bucket (including the bucket’s name, for example ``<bucket-name>`/`<path-to-file>``) containing the data for batch inference. This parameter is not required if your inference type is set to `real-time`. + * `batch_inference_data`: Path to a file in an S3 Bucket (including the bucket’s name, for example `<bucket-name>`/`<path-to-file>`) containing the data for batch inference. This parameter is not required if your inference type is set to `real-time`. @@ -43 +43 @@ The MLOps Workload Orchestrator solution’s AWS API Gateway has two main API en - * `batch_job_output_location`: Path to a prefix in an S3 bucket (including the bucket’s name, for example ``<bucket-name>`/`<prefix>``) to store the output of the batch transform job. This parameter is not required if your inference type is set to `real-time`. + * `batch_job_output_location`: Path to a prefix in an S3 bucket (including the bucket’s name, for example `<bucket-name>`/`<prefix>`) to store the output of the batch transform job. This parameter is not required if your inference type is set to `real-time`. @@ -53 +53 @@ The MLOps Workload Orchestrator solution’s AWS API Gateway has two main API en - * `baseline_job_output_location`: Path to a prefix in an S3 bucket (including the bucket’s name, for example ``<bucket-name>`/`<prefix>``) to store the output of the data baseline job. + * `baseline_job_output_location`: Path to a prefix in an S3 bucket (including the bucket’s name, for example `<bucket-name>`/`<prefix>`) to store the output of the data baseline job. @@ -55 +55 @@ The MLOps Workload Orchestrator solution’s AWS API Gateway has two main API en - * `monitoring_output_location`: Path to a prefix in an S3 bucket (including the bucket’s name, for example ``<bucket-name>`/`<prefix>``) to store the output of the monitoring job. + * `monitoring_output_location`: Path to a prefix in an S3 bucket (including the bucket’s name, for example `<bucket-name>`/`<prefix>`) to store the output of the monitoring job. @@ -63 +63 @@ The MLOps Workload Orchestrator solution’s AWS API Gateway has two main API en - * `kms_key_arn`: Optional customer managed AWS Key Management Service (AWS KMS) key to encrypt captured data from the real-time Amazon SageMaker AI endpoint, output of batch transform and data baseline jobs, output of model monitor, and Amazon Elastic Compute Cloud (Amazon EC2) instance's volume used by Amazon SageMaker AI to run the solution's pipelines. This attribute may be included in the API calls of `byom_realtime_builtin`, `byom_realtime_custom`,` byom_batch_builtin`, `byom_batch_custom`, and `byom_`<monitor-type>`_monitor` pipelines. + * `kms_key_arn`: Optional customer managed AWS Key Management Service (AWS KMS) key to encrypt captured data from the real-time Amazon SageMaker AI endpoint, output of batch transform and data baseline jobs, output of model monitor, and Amazon Elastic Compute Cloud (Amazon EC2) instance’s volume used by Amazon SageMaker AI to run the solution’s pipelines. This attribute may be included in the API calls of `byom_realtime_builtin`, `byom_realtime_custom`,` byom_batch_builtin`, `byom_batch_custom`, and `byom_`[.replaceable]`<monitor-type>``_monitor pipelines`. @@ -71 +71 @@ The MLOps Workload Orchestrator solution’s AWS API Gateway has two main API en - * `problem_type`: Type of Machine learning problem. Valid values are `"Regression"`, `“BinaryClassification”`, or `“MulticlassClassification”`. Used by the model quality, model bias, and model explainability monitoring schedules. It is an optional attribute for the `model_autopilot_training` pipeline. If not provided, the autopilot job will infer the problem type from the `target_attribute`. If provided, the `job_objective` attribute must be provided too. + * `problem_type`: Type of Machine learning problem. Valid values are `"Regression"`, ` "BinaryClassification" `, or ` "MulticlassClassification" `. Used by the model quality, model bias, and model explainability monitoring schedules. It is an optional attribute for the `model_autopilot_training` pipeline. If not provided, the autopilot job will infer the problem type from the `target_attribute`. If provided, the `job_objective` attribute must be provided too. @@ -73 +73 @@ The MLOps Workload Orchestrator solution’s AWS API Gateway has two main API en - * `job_objetive`: (optional) Metric to optimize, used by the `model_autopilot_training` pipeline. If provided, the` problem_type` must be provided. Valid values `"Accuracy"`, `"MSE"`, `"F1"`, `"F1macro"`, `"AUC"`. + * `job_objective`: (optional) Metric to optimize, used by the `model_autopilot_training` pipeline. If provided, the` problem_type` must be provided. Valid values `"Accuracy"`, `"MSE"`, `"F1"`, `"F1macro"`, `"AUC"`. @@ -83 +83 @@ The MLOps Workload Orchestrator solution’s AWS API Gateway has two main API en - * `compression_type`: (optional) Compression type used with the training/validation data. Valid values “Gzip”. + * `compression_type`: (optional) Compression type used with the training/validation data. Valid values "Gzip". @@ -85 +85 @@ The MLOps Workload Orchestrator solution’s AWS API Gateway has two main API en - * `content_type`: (optional) The MIME type of the training data. Default: `“csv”`. + * `content_type`: (optional) The MIME type of the training data. Default: ` "csv" `. @@ -87 +87 @@ The MLOps Workload Orchestrator solution’s AWS API Gateway has two main API en - * `s3_data_type`: (optional) Training S3 data type. Valid values `“S3Prefix”`, `“ManifestFile”`, or `“AugmentedManifestFile”`. Used by the` model_training_builtin` and `model_tuner_builtin` pipelines. Default: `“S3Prefix”`. + * `s3_data_type`: (optional) Training S3 data type. Valid values ` "S3Prefix" `, ` "ManifestFile" `, or ` "AugmentedManifestFile" `. Used by the` model_training_builtin` and `model_tuner_builtin` pipelines. Default: ` "S3Prefix" `. @@ -89 +89 @@ The MLOps Workload Orchestrator solution’s AWS API Gateway has two main API en - * `data_distribution`: (optional) Data distribution. Valid values `“FullyReplicated”` or `“ShardedByS3Key”`. Used by the `model_training_builtin` and `model_tuner_builtin` pipelines. Default: `“FullyReplicated”`. + * `data_distribution`: (optional) Data distribution. Valid values ` "FullyReplicated" ` or ` "ShardedByS3Key" `. Used by the `model_training_builtin` and `model_tuner_builtin` pipelines. Default: ` "FullyReplicated" `. @@ -91 +91 @@ The MLOps Workload Orchestrator solution’s AWS API Gateway has two main API en - * `data_input_mode`: (optional) Training data input mode. Valid `“File”`, `“Pipe”`, `“FastFile”`. Used by the `model_training_builtin` and `model_tuner_builtin` pipelines. Default: `“File”`. + * `data_input_mode`: (optional) Training data input mode. Valid ` "File" `, ` "Pipe" `, ` "FastFile" `. Used by the `model_training_builtin` and `model_tuner_builtin` pipelines. Default: ` "File" `. @@ -93 +93 @@ The MLOps Workload Orchestrator solution’s AWS API Gateway has two main API en - * `data_record_wrapping`: (optional) Training data record wrapping, if any. Valid values `“RecordIO”`. Used by the `model_training_builtin` and `model_tuner_builtin` pipelines. + * `data_record_wrapping`: (optional) Training data record wrapping, if any. Valid values ` "RecordIO" `. Used by the `model_training_builtin` and `model_tuner_builtin` pipelines. @@ -95 +95 @@ The MLOps Workload Orchestrator solution’s AWS API Gateway has two main API en - * `attribute_names`: (optional) List of one or more attribute names to use that are found in a specified `AugmentedManifestFile` (if `s3_data_type = “AugmentedManifestFile”`). Used by the `model_training_builtin` and `model_tuner_builtin` pipelines. + * `attribute_names`: (optional) List of one or more attribute names to use that are found in a specified `AugmentedManifestFile` (if `s3_data_type = "AugmentedManifestFile" `). Used by the `model_training_builtin` and `model_tuner_builtin` pipelines. @@ -105 +105 @@ The MLOps Workload Orchestrator solution’s AWS API Gateway has two main API en - * `generate_definition_only`: (optional) Generate candidate definitions only by the autopilot job. Used by the `model_autopilot_training` pipeline. Default: `“False”`. + * `generate_definition_only`: (optional) Generate candidate definitions only by the autopilot job. Used by the `model_autopilot_training` pipeline. Default: ` "False" `. @@ -107 +107 @@ The MLOps Workload Orchestrator solution’s AWS API Gateway has two main API en - * `encrypt_inner_traffic`: (optional) Encrypt inner-container traffic for the job. Used by training pipelines. Default: `“True”`. + * `encrypt_inner_traffic`: (optional) Encrypt inner-container traffic for the job. Used by training pipelines. Default: ` "True" `. @@ -109 +109 @@ The MLOps Workload Orchestrator solution’s AWS API Gateway has two main API en - * `use_spot_instances`: (optional) Use managed spot instances with the training job. Used by the `model_training_builtin` and `model_tuner_builtin` pipelines. Default: `“True”`. + * `use_spot_instances`: (optional) Use managed spot instances with the training job. Used by the `model_training_builtin` and `model_tuner_builtin` pipelines. Default: ` "True" `. @@ -123 +123 @@ Note: Some have default values and are not required to be specified. Example: {` -Example: {`“min_child_weight”: [“continuous”,[0, 120]]`, `“max_depth”: [“integer”,[1, 15]]`, `“optimizer”: [“categorical”`, `[“sgd”, “Adam”]])`} +Example: {` "min_child_weight": ["continuous",[0, 120]]`, ` "max_depth": ["integer",[1, 15]]`, ` "optimizer": ["categorical" `, `["sgd", "Adam"]])`} @@ -137 +137 @@ Example: {`“min_child_weight”: [“continuous”,[0, 120]]`, `“max_depth - * `shap_config`: a JSON object representing the attributes of [sagemaker.clarify.SHAPConfig](https://sagemaker.readthedocs.io/en/stable/api/training/processing.html#sagemaker.clarify.SHAPConfig). Required only for model explainability monitor. For the `“baseline”` attribute, you can provide a list of lists or as s3 csv file’s key (representing features values to be used as the baseline dataset in the kernel SHAP algorithm). If a file key is provided, the file must be uploaded to the solution’s S3 assets bucket before making the API call. + * `shap_config`: a JSON object representing the attributes of [sagemaker.clarify.SHAPConfig](https://sagemaker.readthedocs.io/en/stable/api/training/processing.html#sagemaker.clarify.SHAPConfig). Required only for model explainability monitor. For the ` "baseline" ` attribute, you can provide a list of lists or as s3 csv file’s key (representing features values to be used as the baseline dataset in the kernel SHAP algorithm). If a file key is provided, the file must be uploaded to the solution’s S3 assets bucket before making the API call. @@ -278 +278 @@ Example: {`“min_child_weight”: [“continuous”,[0, 120]]`, `“max_depth - { + { @@ -282,7 +282,7 @@ Example: {`“min_child_weight”: [“continuous”,[0, 120]]`, `“max_depth - "model_framework_version": "1", - "job_output_location": "<s3-prefix-in-assets-bucket>", - "training_data": "<path/to/training_data.csv>", - "validation_data": "<path/to/validation_data.csv>", - "algo_hyperparamaters": "<algo-hyperparameters-json-object>", - "tuner_configs": "<tuner-configs-json-object>", - "hyperparamaters_ranges": "<hyperparamaters-ranges-json-object>" + "model_framework_version": "1", + "job_output_location": "<s3-prefix-in-assets-bucket>", + "training_data": "<path/to/training_data.csv>", + "validation_data": "<path/to/validation_data.csv>", + "algo_hyperparamaters": "<algo-hyperparameters-json-object>", + "tuner_configs": "<tuner-configs-json-object>", + "hyperparamaters_ranges": "<hyperparamaters-ranges-json-object>" @@ -293 +293 @@ Example: {`“min_child_weight”: [“continuous”,[0, 120]]`, `“max_depth - { + { @@ -295,2 +295,2 @@ Example: {`“min_child_weight”: [“continuous”,[0, 120]]`, `“max_depth - "model_name": "<my-model-name>", - "job_output_location": "<s3-prefix-in-assets-bucket>", + "model_name": "<my-model-name>", + "job_output_location": "<s3-prefix-in-assets-bucket>", @@ -298 +298 @@ Example: {`“min_child_weight”: [“continuous”,[0, 120]]`, `“max_depth - "target_attribute": "<target-attribute-name>" + "target_attribute": "<target-attribute-name>" @@ -361 +361 @@ Example: {`“min_child_weight”: [“continuous”,[0, 120]]`, `“max_depth - "schedule_expression”: "cron(0 * ? * * *)", + "schedule_expression": "cron(0 * ? * * *)", @@ -370 +370 @@ Example: {`“min_child_weight”: [“continuous”,[0, 120]]`, `“max_depth - { + { @@ -394 +394 @@ Example: {`“min_child_weight”: [“continuous”,[0, 120]]`, `“max_depth - { + { @@ -422 +422 @@ Example: {`“min_child_weight”: [“continuous”,[0, 120]]`, `“max_depth - "data_capture_location”: "<bucket-name>/<prefix>", + "data_capture_location": "<bucket-name>/<prefix>", @@ -449 +449 @@ Example: {`“min_child_weight”: [“continuous”,[0, 120]]`, `“max_depth - "data_capture_location”: "<bucket-name>/<prefix>", + "data_capture_location": "<bucket-name>/<prefix>", @@ -474 +474 @@ Example: {`“min_child_weight”: [“continuous”,[0, 120]]`, `“max_depth - "data_capture_location”: "<bucket-name>/<prefix>", + "data_capture_location": "<bucket-name>/<prefix>", @@ -500 +500 @@ Example: {`“min_child_weight”: [“continuous”,[0, 120]]`, `“max_depth - * Model card's `create` operation: + * Model card’s `create` operation: @@ -559 +559 @@ Example: {`“min_child_weight”: [“continuous”,[0, 120]]`, `“max_depth - * Model card's `describe` operation: + * Model card’s `describe` operation: @@ -566 +566 @@ Example: {`“min_child_weight”: [“continuous”,[0, 120]]`, `“max_depth - * Model card's `delete` operation: + * Model card’s `delete` operation: @@ -573 +573 @@ Example: {`“min_child_weight”: [“continuous”,[0, 120]]`, `“max_depth - * Model card's `update` operation: + * Model card’s `update` operation: @@ -584 +584 @@ Example: {`“min_child_weight”: [“continuous”,[0, 120]]`, `“max_depth - * Model card's `export` operation: + * Model card’s `export` operation: @@ -591 +591 @@ Example: {`“min_child_weight”: [“continuous”,[0, 120]]`, `“max_depth - * Model card's list cards: + * Model card’s list cards: @@ -613 +613,5 @@ Expected responses of API requests to `/provisonpipeline`: - * If the pipeline is provisioned for the first time (that is, if no existing pipeline with the same name), the response is: ++ + + * If the pipeline is provisioned for the first time (that is, if no existing pipeline with the same name), the response is: ** + + @@ -615 +619,3 @@ Expected responses of API requests to `/provisonpipeline`: - * { + + + { @@ -639 +643 @@ Expected responses of API requests to `/provisonpipeline`: - "message": "Pipeline <stack-name> is already provisioned. No updates are to be performed. + "message": "Pipeline <stack-name> is already provisioned. No updates are to be performed." @@ -685 +689 @@ Expected responses of API requests to `/provisonpipeline`: - "summary": "Amazon S3 version id: "<id>", + "summary": "Amazon S3 version id: <id>", @@ -711 +715 @@ Expected responses of API requests to `/provisonpipeline`: - "externalExecutionUrl": ""<stack-url>" + "externalExecutionUrl": "<stack-url>" @@ -756 +760 @@ You can use the following API method for inference of the deployed real-time inf - * The request returns a single prediction value, if one data point was in the request, and returns multiple prediction values (separated by a “,”), if several data points were sent in the APIs request. + * The request returns a single prediction value, if one data point was in the request, and returns multiple prediction values (separated by a ","), if several data points were sent in the APIs request.