import type { MetadataBearer as __MetadataBearer } from "@smithy/types";
import type { UpdateMLModelInput, UpdateMLModelOutput } from "../models/models_0";
/**
 * @public
 */
export type { __MetadataBearer };
/**
 * @public
 *
 * The input for {@link UpdateMLModelCommand}.
 */
export interface UpdateMLModelCommandInput extends UpdateMLModelInput {
}
/**
 * @public
 *
 * The output of {@link UpdateMLModelCommand}.
 */
export interface UpdateMLModelCommandOutput extends UpdateMLModelOutput, __MetadataBearer {
}
declare const UpdateMLModelCommand_base: {
    new (input: UpdateMLModelCommandInput): import("@smithy/core/client").CommandImpl<UpdateMLModelCommandInput, UpdateMLModelCommandOutput, import("..").MachineLearningClientResolvedConfig, import("..").ServiceInputTypes, import("..").ServiceOutputTypes>;
    new (input: UpdateMLModelCommandInput): import("@smithy/core/client").CommandImpl<UpdateMLModelCommandInput, UpdateMLModelCommandOutput, import("..").MachineLearningClientResolvedConfig, import("..").ServiceInputTypes, import("..").ServiceOutputTypes>;
    getEndpointParameterInstructions(): import("@smithy/types").EndpointParameterInstructions;
};
/**
 * <p>Updates the <code>MLModelName</code> and the <code>ScoreThreshold</code> of an <code>MLModel</code>.</p>
 *         <p>You can use the <code>GetMLModel</code> operation to view the contents of the updated data element.</p>
 * @example
 * Use a bare-bones client and the command you need to make an API call.
 * ```javascript
 * import { MachineLearningClient, UpdateMLModelCommand } from "@aws-sdk/client-machine-learning"; // ES Modules import
 * // const { MachineLearningClient, UpdateMLModelCommand } = 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 = { // UpdateMLModelInput
 *   MLModelId: "STRING_VALUE", // required
 *   MLModelName: "STRING_VALUE",
 *   ScoreThreshold: Number("float"),
 * };
 * const command = new UpdateMLModelCommand(input);
 * const response = await client.send(command);
 * // { // UpdateMLModelOutput
 * //   MLModelId: "STRING_VALUE",
 * // };
 *
 * ```
 *
 * @param UpdateMLModelCommandInput - {@link UpdateMLModelCommandInput}
 * @returns {@link UpdateMLModelCommandOutput}
 * @see {@link UpdateMLModelCommandInput} for command's `input` shape.
 * @see {@link UpdateMLModelCommandOutput} for command's `response` shape.
 * @see {@link MachineLearningClientResolvedConfig | config} for MachineLearningClient's `config` shape.
 *
 * @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 ResourceNotFoundException} (client fault)
 *  <p>A specified resource cannot be located.</p>
 *
 * @throws {@link MachineLearningServiceException}
 * <p>Base exception class for all service exceptions from MachineLearning service.</p>
 *
 *
 * @public
 */
export declare class UpdateMLModelCommand extends UpdateMLModelCommand_base {
    /** @internal type navigation helper, not in runtime. */
    protected static __types: {
        api: {
            input: UpdateMLModelInput;
            output: UpdateMLModelOutput;
        };
        sdk: {
            input: UpdateMLModelCommandInput;
            output: UpdateMLModelCommandOutput;
        };
    };
}