What describes the typical workflow for analyzing data in Oracle Machine Learning?

The workflow for analyzing data in Oracle Machine Learning typically encompasses several systematic steps that facilitate effective model development and implementation. The correct sequence begins with preparing the data, which includes cleansing and organizing the raw data to ensure it is suitable for analysis. This step is crucial because the quality and structure of the data directly impact the model's performance.

Once the data is prepared, the next step is to create the model. This involves selecting the appropriate algorithms and configuring model parameters based on the specific requirements of the analysis. Following this, the model undergoes training and evaluation phases. During training, the model learns from the prepared dataset, and the evaluation phase focuses on assessing the model’s performance using various metrics to determine its accuracy and effectiveness.

The final step in the workflow is the deployment of the trained model, making it available for use in real-world applications or further analysis. By following this structured approach, users can ensure that the model not only performs well but is also reliable and applicable in decision-making processes.

Other choices present variations in the order of these crucial steps, which can lead to inefficiencies or suboptimal model performance. For instance, starting with predictions or training before data preparation can compromise the quality of the model, as it relies heavily on well-structured and