public CreateAutoMLJobV2Request withAutoMLJobObjective(AutoMLJobObjective autoMLJobObjective) {
        setAutoMLJobObjective(autoMLJobObjective);
        return this;
    }
<p>
Specifies a metric to minimize or maximize as the objective of a job. If not specified, the default objective
metric depends on the problem type. For the list of default values per problem type, see <a
href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_AutoMLJobObjective.html"
>AutoMLJobObjective</a>.
</p>
<note>
<ul>
<li>
<p>
For tabular problem types: You must either provide both the <code>AutoMLJobObjective</code> and indicate the type
of supervised learning problem in <code>AutoMLProblemTypeConfig</code> (<code>TabularJobConfig.ProblemType</code>
), or none at all.
</p>
</li>
<li>
<p>
For text generation problem types (LLMs fine-tuning): Fine-tuning language models in Autopilot does not require
setting the <code>AutoMLJobObjective</code> field. Autopilot fine-tunes LLMs without requiring multiple
candidates to be trained and evaluated. Instead, using your dataset, Autopilot directly fine-tunes your target
model to enhance a default objective metric, the cross-entropy loss. After fine-tuning a language model, you can
evaluate the quality of its generated text using different metrics. For a list of the available metrics, see <a
href="https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-llms-finetuning-metrics.html">Metrics for
fine-tuning LLMs in Autopilot</a>.
</p>
</li>
</ul>
</note>

@param autoMLJobObjective
       Specifies a metric to minimize or maximize as the objective of a job. If not specified, the default
       objective metric depends on the problem type. For the list of default values per problem type, see <a
       href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_AutoMLJobObjective.html">
       AutoMLJobObjective</a>.</p> <note>
       <ul>
       <li>
       <p>
       For tabular problem types: You must either provide both the <code>AutoMLJobObjective</code> and indicate
       the type of supervised learning problem in <code>AutoMLProblemTypeConfig</code> (
       <code>TabularJobConfig.ProblemType</code>), or none at all.
       </p>
       </li>
       <li>
       <p>
       For text generation problem types (LLMs fine-tuning): Fine-tuning language models in Autopilot does not
       require setting the <code>AutoMLJobObjective</code> field. Autopilot fine-tunes LLMs without requiring
       multiple candidates to be trained and evaluated. Instead, using your dataset, Autopilot directly
       fine-tunes your target model to enhance a default objective metric, the cross-entropy loss. After
       fine-tuning a language model, you can evaluate the quality of its generated text using different metrics.
       For a list of the available metrics, see <a
       href="https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-llms-finetuning-metrics.html">Metrics for
       fine-tuning LLMs in Autopilot</a>.
       </p>
       </li>
       </ul>
@return Returns a reference to this object so that method calls can be chained together.
aws-java-sdk-sagemaker/src/main/java/com/amazonaws/services/sagemaker/model/CreateAutoMLJobV2Request.java