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feat(client-sagemaker): Releasing large data support as part of CreateAutoMLJobV2 in SageMaker Autopilot and CreateDomain API for SageMaker Canvas.
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‎clients/client-sagemaker/src/commands/CreateAutoMLJobCommand.ts‎

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@@ -29,6 +29,18 @@ export interface CreateAutoMLJobCommandOutput extends CreateAutoMLJobResponse, _
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/**
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* <p>Creates an Autopilot job also referred to as Autopilot experiment or AutoML job.</p>
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* <p>An AutoML job in SageMaker is a fully automated process that allows you to build machine
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* learning models with minimal effort and machine learning expertise. When initiating an
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* AutoML job, you provide your data and optionally specify parameters tailored to your use
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* case. SageMaker then automates the entire model development lifecycle, including data
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* preprocessing, model training, tuning, and evaluation. AutoML jobs are designed to simplify
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* and accelerate the model building process by automating various tasks and exploring
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* different combinations of machine learning algorithms, data preprocessing techniques, and
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* hyperparameter values. The output of an AutoML job comprises one or more trained models
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* ready for deployment and inference. Additionally, SageMaker AutoML jobs generate a candidate
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* model leaderboard, allowing you to select the best-performing model for deployment.</p>
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* <p>For more information about AutoML jobs, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-automate-model-development.html">https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-automate-model-development.html</a>
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* in the SageMaker developer guide.</p>
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* <note>
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* <p>We recommend using the new versions <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateAutoMLJobV2.html">CreateAutoMLJobV2</a> and <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeAutoMLJobV2.html">DescribeAutoMLJobV2</a>, which offer backward compatibility.</p>
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* <p>

‎clients/client-sagemaker/src/commands/CreateAutoMLJobV2Command.ts‎

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@@ -5,7 +5,7 @@ import { Command as $Command } from "@smithy/smithy-client";
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import { MetadataBearer as __MetadataBearer } from "@smithy/types";
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import { commonParams } from "../endpoint/EndpointParameters";
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import { CreateAutoMLJobV2Request, CreateAutoMLJobV2Response } from "../models/models_0";
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import { CreateAutoMLJobV2Request, CreateAutoMLJobV2Response } from "../models/models_1";
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import { de_CreateAutoMLJobV2Command, se_CreateAutoMLJobV2Command } from "../protocols/Aws_json1_1";
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import { SageMakerClientResolvedConfig, ServiceInputTypes, ServiceOutputTypes } from "../SageMakerClient";
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@@ -29,6 +29,21 @@ export interface CreateAutoMLJobV2CommandOutput extends CreateAutoMLJobV2Respons
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/**
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* <p>Creates an Autopilot job also referred to as Autopilot experiment or AutoML job V2.</p>
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* <p>An AutoML job in SageMaker is a fully automated process that allows you to build machine
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* learning models with minimal effort and machine learning expertise. When initiating an
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* AutoML job, you provide your data and optionally specify parameters tailored to your use
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* case. SageMaker then automates the entire model development lifecycle, including data
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* preprocessing, model training, tuning, and evaluation. AutoML jobs are designed to simplify
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* and accelerate the model building process by automating various tasks and exploring
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* different combinations of machine learning algorithms, data preprocessing techniques, and
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* hyperparameter values. The output of an AutoML job comprises one or more trained models
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* ready for deployment and inference. Additionally, SageMaker AutoML jobs generate a candidate
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* model leaderboard, allowing you to select the best-performing model for deployment.</p>
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* <p>For more information about AutoML jobs, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-automate-model-development.html">https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-automate-model-development.html</a>
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* in the SageMaker developer guide.</p>
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* <p>AutoML jobs V2 support various problem types such as regression, binary, and multiclass
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* classification with tabular data, text and image classification, time-series forecasting,
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* and fine-tuning of large language models (LLMs) for text generation.</p>
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* <note>
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* <p>
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* <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateAutoMLJobV2.html">CreateAutoMLJobV2</a> and <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeAutoMLJobV2.html">DescribeAutoMLJobV2</a> are new versions of <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateAutoMLJob.html">CreateAutoMLJob</a>
@@ -197,6 +212,11 @@ export interface CreateAutoMLJobV2CommandOutput extends CreateAutoMLJobV2Respons
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* DataSplitConfig: { // AutoMLDataSplitConfig
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* ValidationFraction: Number("float"),
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* },
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* AutoMLComputeConfig: { // AutoMLComputeConfig
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* EmrServerlessComputeConfig: { // EmrServerlessComputeConfig
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* ExecutionRoleARN: "STRING_VALUE", // required
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* },
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* },
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* };
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* const command = new CreateAutoMLJobV2Command(input);
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* const response = await client.send(command);

‎clients/client-sagemaker/src/commands/CreateClusterCommand.ts‎

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@@ -5,8 +5,7 @@ import { Command as $Command } from "@smithy/smithy-client";
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import { MetadataBearer as __MetadataBearer } from "@smithy/types";
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import { commonParams } from "../endpoint/EndpointParameters";
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import { CreateClusterRequest } from "../models/models_0";
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import { CreateClusterResponse } from "../models/models_1";
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import { CreateClusterRequest, CreateClusterResponse } from "../models/models_1";
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import { de_CreateClusterCommand, se_CreateClusterCommand } from "../protocols/Aws_json1_1";
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import { SageMakerClientResolvedConfig, ServiceInputTypes, ServiceOutputTypes } from "../SageMakerClient";
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‎clients/client-sagemaker/src/commands/CreateDomainCommand.ts‎

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@@ -185,6 +185,10 @@ export interface CreateDomainCommandOutput extends CreateDomainResponse, __Metad
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* GenerativeAiSettings: { // GenerativeAiSettings
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* AmazonBedrockRoleArn: "STRING_VALUE",
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* },
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* EmrServerlessSettings: { // EmrServerlessSettings
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* ExecutionRoleArn: "STRING_VALUE",
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* Status: "ENABLED" || "DISABLED",
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* },
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* },
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* CodeEditorAppSettings: { // CodeEditorAppSettings
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* DefaultResourceSpec: {
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* ],
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* StudioWebPortalSettings: { // StudioWebPortalSettings
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* HiddenMlTools: [ // HiddenMlToolsList
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* "DataWrangler" || "FeatureStore" || "EmrClusters" || "AutoMl" || "Experiments" || "Training" || "ModelEvaluation" || "Pipelines" || "Models" || "JumpStart" || "InferenceRecommender" || "Endpoints" || "Projects",
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* "DataWrangler" || "FeatureStore" || "EmrClusters" || "AutoMl" || "Experiments" || "Training" || "ModelEvaluation" || "Pipelines" || "Models" || "JumpStart" || "InferenceRecommender" || "Endpoints" || "Projects" || "InferenceOptimization",
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* ],
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* HiddenAppTypes: [ // HiddenAppTypesList
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* "JupyterServer" || "KernelGateway" || "DetailedProfiler" || "TensorBoard" || "CodeEditor" || "JupyterLab" || "RStudioServerPro" || "RSessionGateway" || "Canvas",

‎clients/client-sagemaker/src/commands/CreateProcessingJobCommand.ts‎

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@@ -82,7 +82,7 @@ export interface CreateProcessingJobCommandOutput extends CreateProcessingJobRes
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* OutputName: "STRING_VALUE", // required
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* S3Output: { // ProcessingS3Output
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* S3Uri: "STRING_VALUE", // required
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* LocalPath: "STRING_VALUE", // required
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* LocalPath: "STRING_VALUE",
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* S3UploadMode: "Continuous" || "EndOfJob", // required
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* },
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* FeatureStoreOutput: { // ProcessingFeatureStoreOutput

‎clients/client-sagemaker/src/commands/CreateUserProfileCommand.ts‎

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@@ -156,6 +156,10 @@ export interface CreateUserProfileCommandOutput extends CreateUserProfileRespons
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* GenerativeAiSettings: { // GenerativeAiSettings
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* AmazonBedrockRoleArn: "STRING_VALUE",
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* },
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* EmrServerlessSettings: { // EmrServerlessSettings
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* ExecutionRoleArn: "STRING_VALUE",
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* Status: "ENABLED" || "DISABLED",
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* },
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* },
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* CodeEditorAppSettings: { // CodeEditorAppSettings
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* DefaultResourceSpec: {
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* ],
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* StudioWebPortalSettings: { // StudioWebPortalSettings
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* HiddenMlTools: [ // HiddenMlToolsList
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* "DataWrangler" || "FeatureStore" || "EmrClusters" || "AutoMl" || "Experiments" || "Training" || "ModelEvaluation" || "Pipelines" || "Models" || "JumpStart" || "InferenceRecommender" || "Endpoints" || "Projects",
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* "DataWrangler" || "FeatureStore" || "EmrClusters" || "AutoMl" || "Experiments" || "Training" || "ModelEvaluation" || "Pipelines" || "Models" || "JumpStart" || "InferenceRecommender" || "Endpoints" || "Projects" || "InferenceOptimization",
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* ],
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* HiddenAppTypes: [ // HiddenAppTypesList
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* "JupyterServer" || "KernelGateway" || "DetailedProfiler" || "TensorBoard" || "CodeEditor" || "JupyterLab" || "RStudioServerPro" || "RSessionGateway" || "Canvas",

‎clients/client-sagemaker/src/commands/DescribeAutoMLJobV2Command.ts‎

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@@ -273,6 +273,11 @@ export interface DescribeAutoMLJobV2CommandOutput extends DescribeAutoMLJobV2Res
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* // ],
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* // },
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* // },
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* // AutoMLComputeConfig: { // AutoMLComputeConfig
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* // EmrServerlessComputeConfig: { // EmrServerlessComputeConfig
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* // ExecutionRoleARN: "STRING_VALUE", // required
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* // },
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* // },
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* // };
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*
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* ```

‎clients/client-sagemaker/src/commands/DescribeDomainCommand.ts‎

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@@ -157,6 +157,10 @@ export interface DescribeDomainCommandOutput extends DescribeDomainResponse, __M
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* // GenerativeAiSettings: { // GenerativeAiSettings
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* // AmazonBedrockRoleArn: "STRING_VALUE",
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* // },
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* // EmrServerlessSettings: { // EmrServerlessSettings
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* // ExecutionRoleArn: "STRING_VALUE",
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* // Status: "ENABLED" || "DISABLED",
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* // },
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* // },
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* // CodeEditorAppSettings: { // CodeEditorAppSettings
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* // DefaultResourceSpec: {
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* // ],
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* // StudioWebPortalSettings: { // StudioWebPortalSettings
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* // HiddenMlTools: [ // HiddenMlToolsList
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* // "DataWrangler" || "FeatureStore" || "EmrClusters" || "AutoMl" || "Experiments" || "Training" || "ModelEvaluation" || "Pipelines" || "Models" || "JumpStart" || "InferenceRecommender" || "Endpoints" || "Projects",
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* // "DataWrangler" || "FeatureStore" || "EmrClusters" || "AutoMl" || "Experiments" || "Training" || "ModelEvaluation" || "Pipelines" || "Models" || "JumpStart" || "InferenceRecommender" || "Endpoints" || "Projects" || "InferenceOptimization",
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* // ],
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* // HiddenAppTypes: [ // HiddenAppTypesList
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* // "JupyterServer" || "KernelGateway" || "DetailedProfiler" || "TensorBoard" || "CodeEditor" || "JupyterLab" || "RStudioServerPro" || "RSessionGateway" || "Canvas",

‎clients/client-sagemaker/src/commands/DescribeModelCardCommand.ts‎

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@@ -5,11 +5,8 @@ import { Command as $Command } from "@smithy/smithy-client";
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import { MetadataBearer as __MetadataBearer } from "@smithy/types";
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import { commonParams } from "../endpoint/EndpointParameters";
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import {
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DescribeModelCardRequest,
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DescribeModelCardResponse,
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DescribeModelCardResponseFilterSensitiveLog,
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} from "../models/models_2";
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import { DescribeModelCardRequest } from "../models/models_2";
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import { DescribeModelCardResponse, DescribeModelCardResponseFilterSensitiveLog } from "../models/models_3";
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import { de_DescribeModelCardCommand, se_DescribeModelCardCommand } from "../protocols/Aws_json1_1";
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import { SageMakerClientResolvedConfig, ServiceInputTypes, ServiceOutputTypes } from "../SageMakerClient";
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‎clients/client-sagemaker/src/commands/DescribeModelCardExportJobCommand.ts‎

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@@ -5,8 +5,7 @@ import { Command as $Command } from "@smithy/smithy-client";
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import { MetadataBearer as __MetadataBearer } from "@smithy/types";
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import { commonParams } from "../endpoint/EndpointParameters";
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import { DescribeModelCardExportJobRequest } from "../models/models_2";
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import { DescribeModelCardExportJobResponse } from "../models/models_3";
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import { DescribeModelCardExportJobRequest, DescribeModelCardExportJobResponse } from "../models/models_3";
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import { de_DescribeModelCardExportJobCommand, se_DescribeModelCardExportJobCommand } from "../protocols/Aws_json1_1";
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import { SageMakerClientResolvedConfig, ServiceInputTypes, ServiceOutputTypes } from "../SageMakerClient";
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