/**
* Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
* SPDX-License-Identifier: Apache-2.0.
*/
#pragma once
#include A set of recommended deployment configurations for the model. To get more
* advanced recommendations, see CreateInferenceRecommendationsJob
* to create an inference recommendation job.See Also:
AWS
* API Reference
Status of the deployment recommendation. The status
* NOT_APPLICABLE
means that SageMaker is unable to provide a default
* recommendation for the model using the information provided. If the deployment
* status is IN_PROGRESS
, retry your API call after a few seconds to
* get a COMPLETED
deployment recommendation.
Status of the deployment recommendation. The status
* NOT_APPLICABLE
means that SageMaker is unable to provide a default
* recommendation for the model using the information provided. If the deployment
* status is IN_PROGRESS
, retry your API call after a few seconds to
* get a COMPLETED
deployment recommendation.
Status of the deployment recommendation. The status
* NOT_APPLICABLE
means that SageMaker is unable to provide a default
* recommendation for the model using the information provided. If the deployment
* status is IN_PROGRESS
, retry your API call after a few seconds to
* get a COMPLETED
deployment recommendation.
Status of the deployment recommendation. The status
* NOT_APPLICABLE
means that SageMaker is unable to provide a default
* recommendation for the model using the information provided. If the deployment
* status is IN_PROGRESS
, retry your API call after a few seconds to
* get a COMPLETED
deployment recommendation.
Status of the deployment recommendation. The status
* NOT_APPLICABLE
means that SageMaker is unable to provide a default
* recommendation for the model using the information provided. If the deployment
* status is IN_PROGRESS
, retry your API call after a few seconds to
* get a COMPLETED
deployment recommendation.
Status of the deployment recommendation. The status
* NOT_APPLICABLE
means that SageMaker is unable to provide a default
* recommendation for the model using the information provided. If the deployment
* status is IN_PROGRESS
, retry your API call after a few seconds to
* get a COMPLETED
deployment recommendation.
A list of RealTimeInferenceRecommendation * items.
*/ inline const Aws::VectorA list of RealTimeInferenceRecommendation * items.
*/ inline bool RealTimeInferenceRecommendationsHasBeenSet() const { return m_realTimeInferenceRecommendationsHasBeenSet; } /** *A list of RealTimeInferenceRecommendation * items.
*/ inline void SetRealTimeInferenceRecommendations(const Aws::VectorA list of RealTimeInferenceRecommendation * items.
*/ inline void SetRealTimeInferenceRecommendations(Aws::VectorA list of RealTimeInferenceRecommendation * items.
*/ inline DeploymentRecommendation& WithRealTimeInferenceRecommendations(const Aws::VectorA list of RealTimeInferenceRecommendation * items.
*/ inline DeploymentRecommendation& WithRealTimeInferenceRecommendations(Aws::VectorA list of RealTimeInferenceRecommendation * items.
*/ inline DeploymentRecommendation& AddRealTimeInferenceRecommendations(const RealTimeInferenceRecommendation& value) { m_realTimeInferenceRecommendationsHasBeenSet = true; m_realTimeInferenceRecommendations.push_back(value); return *this; } /** *A list of RealTimeInferenceRecommendation * items.
*/ inline DeploymentRecommendation& AddRealTimeInferenceRecommendations(RealTimeInferenceRecommendation&& value) { m_realTimeInferenceRecommendationsHasBeenSet = true; m_realTimeInferenceRecommendations.push_back(std::move(value)); return *this; } private: RecommendationStatus m_recommendationStatus; bool m_recommendationStatusHasBeenSet = false; Aws::Vector