import { Command as $Command } from "@smithy/smithy-client";
import { MetadataBearer as __MetadataBearer } from "@smithy/types";
import { CreateAutoMLJobV2Request, CreateAutoMLJobV2Response } from "../models/models_1";
import { SageMakerClientResolvedConfig, ServiceInputTypes, ServiceOutputTypes } from "../SageMakerClient";
/**
 * @public
 */
export type { __MetadataBearer };
export { $Command };
/**
 * @public
 *
 * The input for {@link CreateAutoMLJobV2Command}.
 */
export interface CreateAutoMLJobV2CommandInput extends CreateAutoMLJobV2Request {
}
/**
 * @public
 *
 * The output of {@link CreateAutoMLJobV2Command}.
 */
export interface CreateAutoMLJobV2CommandOutput extends CreateAutoMLJobV2Response, __MetadataBearer {
}
declare const CreateAutoMLJobV2Command_base: {
    new (input: CreateAutoMLJobV2CommandInput): import("@smithy/smithy-client").CommandImpl<CreateAutoMLJobV2CommandInput, CreateAutoMLJobV2CommandOutput, SageMakerClientResolvedConfig, ServiceInputTypes, ServiceOutputTypes>;
    new (input: CreateAutoMLJobV2CommandInput): import("@smithy/smithy-client").CommandImpl<CreateAutoMLJobV2CommandInput, CreateAutoMLJobV2CommandOutput, SageMakerClientResolvedConfig, ServiceInputTypes, ServiceOutputTypes>;
    getEndpointParameterInstructions(): import("@smithy/middleware-endpoint").EndpointParameterInstructions;
};
/**
 * <p>Creates an Autopilot job also referred to as Autopilot experiment or AutoML job V2.</p> <p>An AutoML job in SageMaker AI is a fully automated process that allows you to build machine learning models with minimal effort and machine learning expertise. When initiating an AutoML job, you provide your data and optionally specify parameters tailored to your use case. SageMaker AI then automates the entire model development lifecycle, including data preprocessing, model training, tuning, and evaluation. AutoML jobs are designed to simplify and accelerate the model building process by automating various tasks and exploring different combinations of machine learning algorithms, data preprocessing techniques, and hyperparameter values. The output of an AutoML job comprises one or more trained models ready for deployment and inference. Additionally, SageMaker AI AutoML jobs generate a candidate model leaderboard, allowing you to select the best-performing model for deployment.</p> <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> in the SageMaker AI developer guide.</p> <p>AutoML jobs V2 support various problem types such as regression, binary, and multiclass classification with tabular data, text and image classification, time-series forecasting, and fine-tuning of large language models (LLMs) for text generation.</p> <note> <p> <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> and <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeAutoMLJob.html">DescribeAutoMLJob</a> which offer backward compatibility.</p> <p> <code>CreateAutoMLJobV2</code> can manage tabular problem types identical to those of its previous version <code>CreateAutoMLJob</code>, as well as time-series forecasting, non-tabular problem types such as image or text classification, and text generation (LLMs fine-tuning).</p> <p>Find guidelines about how to migrate a <code>CreateAutoMLJob</code> to <code>CreateAutoMLJobV2</code> in <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-automate-model-development-create-experiment.html#autopilot-create-experiment-api-migrate-v1-v2">Migrate a CreateAutoMLJob to CreateAutoMLJobV2</a>.</p> </note> <p>For the list of available problem types supported by <code>CreateAutoMLJobV2</code>, see <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_AutoMLProblemTypeConfig.html">AutoMLProblemTypeConfig</a>.</p> <p>You can find the best-performing model after you run an AutoML job V2 by calling <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeAutoMLJobV2.html">DescribeAutoMLJobV2</a>.</p>
 * @example
 * Use a bare-bones client and the command you need to make an API call.
 * ```javascript
 * import { SageMakerClient, CreateAutoMLJobV2Command } from "@aws-sdk/client-sagemaker"; // ES Modules import
 * // const { SageMakerClient, CreateAutoMLJobV2Command } = require("@aws-sdk/client-sagemaker"); // CommonJS import
 * const client = new SageMakerClient(config);
 * const input = { // CreateAutoMLJobV2Request
 *   AutoMLJobName: "STRING_VALUE", // required
 *   AutoMLJobInputDataConfig: [ // AutoMLJobInputDataConfig // required
 *     { // AutoMLJobChannel
 *       ChannelType: "training" || "validation",
 *       ContentType: "STRING_VALUE",
 *       CompressionType: "None" || "Gzip",
 *       DataSource: { // AutoMLDataSource
 *         S3DataSource: { // AutoMLS3DataSource
 *           S3DataType: "ManifestFile" || "S3Prefix" || "AugmentedManifestFile", // required
 *           S3Uri: "STRING_VALUE", // required
 *         },
 *       },
 *     },
 *   ],
 *   OutputDataConfig: { // AutoMLOutputDataConfig
 *     KmsKeyId: "STRING_VALUE",
 *     S3OutputPath: "STRING_VALUE", // required
 *   },
 *   AutoMLProblemTypeConfig: { // AutoMLProblemTypeConfig Union: only one key present
 *     ImageClassificationJobConfig: { // ImageClassificationJobConfig
 *       CompletionCriteria: { // AutoMLJobCompletionCriteria
 *         MaxCandidates: Number("int"),
 *         MaxRuntimePerTrainingJobInSeconds: Number("int"),
 *         MaxAutoMLJobRuntimeInSeconds: Number("int"),
 *       },
 *     },
 *     TextClassificationJobConfig: { // TextClassificationJobConfig
 *       CompletionCriteria: {
 *         MaxCandidates: Number("int"),
 *         MaxRuntimePerTrainingJobInSeconds: Number("int"),
 *         MaxAutoMLJobRuntimeInSeconds: Number("int"),
 *       },
 *       ContentColumn: "STRING_VALUE", // required
 *       TargetLabelColumn: "STRING_VALUE", // required
 *     },
 *     TimeSeriesForecastingJobConfig: { // TimeSeriesForecastingJobConfig
 *       FeatureSpecificationS3Uri: "STRING_VALUE",
 *       CompletionCriteria: {
 *         MaxCandidates: Number("int"),
 *         MaxRuntimePerTrainingJobInSeconds: Number("int"),
 *         MaxAutoMLJobRuntimeInSeconds: Number("int"),
 *       },
 *       ForecastFrequency: "STRING_VALUE", // required
 *       ForecastHorizon: Number("int"), // required
 *       ForecastQuantiles: [ // ForecastQuantiles
 *         "STRING_VALUE",
 *       ],
 *       Transformations: { // TimeSeriesTransformations
 *         Filling: { // FillingTransformations
 *           "<keys>": { // FillingTransformationMap
 *             "<keys>": "STRING_VALUE",
 *           },
 *         },
 *         Aggregation: { // AggregationTransformations
 *           "<keys>": "sum" || "avg" || "first" || "min" || "max",
 *         },
 *       },
 *       TimeSeriesConfig: { // TimeSeriesConfig
 *         TargetAttributeName: "STRING_VALUE", // required
 *         TimestampAttributeName: "STRING_VALUE", // required
 *         ItemIdentifierAttributeName: "STRING_VALUE", // required
 *         GroupingAttributeNames: [ // GroupingAttributeNames
 *           "STRING_VALUE",
 *         ],
 *       },
 *       HolidayConfig: [ // HolidayConfig
 *         { // HolidayConfigAttributes
 *           CountryCode: "STRING_VALUE",
 *         },
 *       ],
 *       CandidateGenerationConfig: { // CandidateGenerationConfig
 *         AlgorithmsConfig: [ // AutoMLAlgorithmsConfig
 *           { // AutoMLAlgorithmConfig
 *             AutoMLAlgorithms: [ // AutoMLAlgorithms // required
 *               "xgboost" || "linear-learner" || "mlp" || "lightgbm" || "catboost" || "randomforest" || "extra-trees" || "nn-torch" || "fastai" || "cnn-qr" || "deepar" || "prophet" || "npts" || "arima" || "ets",
 *             ],
 *           },
 *         ],
 *       },
 *     },
 *     TabularJobConfig: { // TabularJobConfig
 *       CandidateGenerationConfig: {
 *         AlgorithmsConfig: [
 *           {
 *             AutoMLAlgorithms: [ // required
 *               "xgboost" || "linear-learner" || "mlp" || "lightgbm" || "catboost" || "randomforest" || "extra-trees" || "nn-torch" || "fastai" || "cnn-qr" || "deepar" || "prophet" || "npts" || "arima" || "ets",
 *             ],
 *           },
 *         ],
 *       },
 *       CompletionCriteria: {
 *         MaxCandidates: Number("int"),
 *         MaxRuntimePerTrainingJobInSeconds: Number("int"),
 *         MaxAutoMLJobRuntimeInSeconds: Number("int"),
 *       },
 *       FeatureSpecificationS3Uri: "STRING_VALUE",
 *       Mode: "AUTO" || "ENSEMBLING" || "HYPERPARAMETER_TUNING",
 *       GenerateCandidateDefinitionsOnly: true || false,
 *       ProblemType: "BinaryClassification" || "MulticlassClassification" || "Regression",
 *       TargetAttributeName: "STRING_VALUE", // required
 *       SampleWeightAttributeName: "STRING_VALUE",
 *     },
 *     TextGenerationJobConfig: { // TextGenerationJobConfig
 *       CompletionCriteria: {
 *         MaxCandidates: Number("int"),
 *         MaxRuntimePerTrainingJobInSeconds: Number("int"),
 *         MaxAutoMLJobRuntimeInSeconds: Number("int"),
 *       },
 *       BaseModelName: "STRING_VALUE",
 *       TextGenerationHyperParameters: { // TextGenerationHyperParameters
 *         "<keys>": "STRING_VALUE",
 *       },
 *       ModelAccessConfig: { // ModelAccessConfig
 *         AcceptEula: true || false, // required
 *       },
 *     },
 *   },
 *   RoleArn: "STRING_VALUE", // required
 *   Tags: [ // TagList
 *     { // Tag
 *       Key: "STRING_VALUE", // required
 *       Value: "STRING_VALUE", // required
 *     },
 *   ],
 *   SecurityConfig: { // AutoMLSecurityConfig
 *     VolumeKmsKeyId: "STRING_VALUE",
 *     EnableInterContainerTrafficEncryption: true || false,
 *     VpcConfig: { // VpcConfig
 *       SecurityGroupIds: [ // VpcSecurityGroupIds // required
 *         "STRING_VALUE",
 *       ],
 *       Subnets: [ // Subnets // required
 *         "STRING_VALUE",
 *       ],
 *     },
 *   },
 *   AutoMLJobObjective: { // AutoMLJobObjective
 *     MetricName: "Accuracy" || "MSE" || "F1" || "F1macro" || "AUC" || "RMSE" || "BalancedAccuracy" || "R2" || "Recall" || "RecallMacro" || "Precision" || "PrecisionMacro" || "MAE" || "MAPE" || "MASE" || "WAPE" || "AverageWeightedQuantileLoss", // required
 *   },
 *   ModelDeployConfig: { // ModelDeployConfig
 *     AutoGenerateEndpointName: true || false,
 *     EndpointName: "STRING_VALUE",
 *   },
 *   DataSplitConfig: { // AutoMLDataSplitConfig
 *     ValidationFraction: Number("float"),
 *   },
 *   AutoMLComputeConfig: { // AutoMLComputeConfig
 *     EmrServerlessComputeConfig: { // EmrServerlessComputeConfig
 *       ExecutionRoleARN: "STRING_VALUE", // required
 *     },
 *   },
 * };
 * const command = new CreateAutoMLJobV2Command(input);
 * const response = await client.send(command);
 * // { // CreateAutoMLJobV2Response
 * //   AutoMLJobArn: "STRING_VALUE", // required
 * // };
 *
 * ```
 *
 * @param CreateAutoMLJobV2CommandInput - {@link CreateAutoMLJobV2CommandInput}
 * @returns {@link CreateAutoMLJobV2CommandOutput}
 * @see {@link CreateAutoMLJobV2CommandInput} for command's `input` shape.
 * @see {@link CreateAutoMLJobV2CommandOutput} for command's `response` shape.
 * @see {@link SageMakerClientResolvedConfig | config} for SageMakerClient's `config` shape.
 *
 * @throws {@link ResourceInUse} (client fault)
 *  <p>Resource being accessed is in use.</p>
 *
 * @throws {@link ResourceLimitExceeded} (client fault)
 *  <p> You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created. </p>
 *
 * @throws {@link SageMakerServiceException}
 * <p>Base exception class for all service exceptions from SageMaker service.</p>
 *
 *
 * @public
 */
export declare class CreateAutoMLJobV2Command extends CreateAutoMLJobV2Command_base {
    /** @internal type navigation helper, not in runtime. */
    protected static __types: {
        api: {
            input: CreateAutoMLJobV2Request;
            output: CreateAutoMLJobV2Response;
        };
        sdk: {
            input: CreateAutoMLJobV2CommandInput;
            output: CreateAutoMLJobV2CommandOutput;
        };
    };
}