import type { MetadataBearer as __MetadataBearer } from "@smithy/types";
import type { CreateMLModelInput, CreateMLModelOutput } from "../models/models_0";
/**
 * @public
 */
export type { __MetadataBearer };
/**
 * @public
 *
 * The input for {@link CreateMLModelCommand}.
 */
export interface CreateMLModelCommandInput extends CreateMLModelInput {
}
/**
 * @public
 *
 * The output of {@link CreateMLModelCommand}.
 */
export interface CreateMLModelCommandOutput extends CreateMLModelOutput, __MetadataBearer {
}
declare const CreateMLModelCommand_base: {
    new (input: CreateMLModelCommandInput): import("@smithy/core/client").CommandImpl<CreateMLModelCommandInput, CreateMLModelCommandOutput, import("..").MachineLearningClientResolvedConfig, import("..").ServiceInputTypes, import("..").ServiceOutputTypes>;
    new (input: CreateMLModelCommandInput): import("@smithy/core/client").CommandImpl<CreateMLModelCommandInput, CreateMLModelCommandOutput, import("..").MachineLearningClientResolvedConfig, import("..").ServiceInputTypes, import("..").ServiceOutputTypes>;
    getEndpointParameterInstructions(): import("@smithy/types").EndpointParameterInstructions;
};
/**
 * <p>Creates a new <code>MLModel</code> using the <code>DataSource</code> and the recipe as
 *             information sources. </p>
 *         <p>An <code>MLModel</code> is nearly immutable. Users can update only the
 *                 <code>MLModelName</code> and the <code>ScoreThreshold</code> in an
 *                 <code>MLModel</code> without creating a new <code>MLModel</code>. </p>
 *         <p>
 *             <code>CreateMLModel</code> is an asynchronous operation. In response to
 *                 <code>CreateMLModel</code>, Amazon Machine Learning (Amazon ML) immediately returns
 *             and sets the <code>MLModel</code> status to <code>PENDING</code>. After the
 *                 <code>MLModel</code> has been created and ready is for use, Amazon ML sets the
 *             status to <code>COMPLETED</code>. </p>
 *         <p>You can use the <code>GetMLModel</code> operation to check the progress of the
 *                 <code>MLModel</code> during the creation operation.</p>
 *
 *         <p>
 *            <code>CreateMLModel</code> requires a <code>DataSource</code> with computed statistics,
 *           which can be created by setting <code>ComputeStatistics</code> to <code>true</code> in
 *           <code>CreateDataSourceFromRDS</code>, <code>CreateDataSourceFromS3</code>, or
 *           <code>CreateDataSourceFromRedshift</code> operations.
 *         </p>
 * @example
 * Use a bare-bones client and the command you need to make an API call.
 * ```javascript
 * import { MachineLearningClient, CreateMLModelCommand } from "@aws-sdk/client-machine-learning"; // ES Modules import
 * // const { MachineLearningClient, CreateMLModelCommand } = require("@aws-sdk/client-machine-learning"); // CommonJS import
 * // import type { MachineLearningClientConfig } from "@aws-sdk/client-machine-learning";
 * const config = {}; // type is MachineLearningClientConfig
 * const client = new MachineLearningClient(config);
 * const input = { // CreateMLModelInput
 *   MLModelId: "STRING_VALUE", // required
 *   MLModelName: "STRING_VALUE",
 *   MLModelType: "REGRESSION" || "BINARY" || "MULTICLASS", // required
 *   Parameters: { // TrainingParameters
 *     "<keys>": "STRING_VALUE",
 *   },
 *   TrainingDataSourceId: "STRING_VALUE", // required
 *   Recipe: "STRING_VALUE",
 *   RecipeUri: "STRING_VALUE",
 * };
 * const command = new CreateMLModelCommand(input);
 * const response = await client.send(command);
 * // { // CreateMLModelOutput
 * //   MLModelId: "STRING_VALUE",
 * // };
 *
 * ```
 *
 * @param CreateMLModelCommandInput - {@link CreateMLModelCommandInput}
 * @returns {@link CreateMLModelCommandOutput}
 * @see {@link CreateMLModelCommandInput} for command's `input` shape.
 * @see {@link CreateMLModelCommandOutput} for command's `response` shape.
 * @see {@link MachineLearningClientResolvedConfig | config} for MachineLearningClient's `config` shape.
 *
 * @throws {@link IdempotentParameterMismatchException} (client fault)
 *  <p>A second request to use or change an object was not allowed. This can result from retrying a request using a parameter that was not present in the original request.</p>
 *
 * @throws {@link InternalServerException} (server fault)
 *  <p>An error on the server occurred when trying to process a request.</p>
 *
 * @throws {@link InvalidInputException} (client fault)
 *  <p>An error on the client occurred. Typically, the cause is an invalid input value.</p>
 *
 * @throws {@link MachineLearningServiceException}
 * <p>Base exception class for all service exceptions from MachineLearning service.</p>
 *
 *
 * @public
 */
export declare class CreateMLModelCommand extends CreateMLModelCommand_base {
    /** @internal type navigation helper, not in runtime. */
    protected static __types: {
        api: {
            input: CreateMLModelInput;
            output: CreateMLModelOutput;
        };
        sdk: {
            input: CreateMLModelCommandInput;
            output: CreateMLModelCommandOutput;
        };
    };
}