The sequence represents the standard, fundamental workflow for creating a machine learning model before it can be deployed.

1. Data Preparation: Raw data must first be collected, cleaned, transformed, and structured (e.g., feature engineering, splitting into training and test sets). A model cannot be trained on unprepared data.
2. Model Training: The prepared training dataset is used to "teach" an algorithm, allowing it to learn patterns and create the model.
3. Model Evaluation: After training, the model's performance must be validated using a separate test dataset (which it has not seen before) to measure its accuracy and effectiveness. A model is only deployed if its evaluation results meet the required business objectives.
4. The other options, model retraining and data encryption, are incorrect for this sequence. Model retraining is a post-deployment process performed to update a model when its performance degrades over time (model drift). Data encryption is a security process applied to data at rest or in transit, not a sequential step in the model creation pipeline itself.

Source: Microsoft (n.d.). What is Azure Machine Learning designer? Microsoft Docs.

Reference: In the section titled "Build a pipeline," the documentation describes the visual workflow: "Pipelines start with your data... you can connect these datasets to other modules which prepare data, train models, and evaluate models." This officially confirms the sequential order of these three core processes.

Source: Amershi, S., Begel, A., Bird, C., et al. (2019). Software Engineering for Machine Learning: A Case Study. 2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP).

Reference: Page 292 (Section III.A, "AI Development Workflow"). The paper describes the typical ML workflow stages identified through case studies, stating: "...the AI development workflow includes data preparation (e.g., collect, clean, label), model training (e.g., feature engineering, training), and model evaluation..."

DOI: https://doi.org/10.1109/ICSE-SEIP.2019.00018

Source: Microsoft (n.d.). The machine learning lifecycle. Microsoft Docs.

Reference: The document explicitly outlines the stages of the machine learning lifecycle. The core model creation process is described in this order: 3. Data preparation, 4. Model training, and 5. Model evaluation, which all occur before stage 6. Model deployment. This clearly separates the pre-deployment steps from post-deployment activities like "Model monitoring" (which leads to retraining).