Which of the following is not an essential stage in the machine learning lifecycle?

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Question: Which of the following is not an essential stage in the machine learning lifecycle?

Option 1: Data Visualization. This activity involves representing data visually to gain insights, spot patterns, and communicate findings. While data visualization is valuable for exploratory data analysis and presenting results, it is not typically listed as a core, required stage of the ML lifecycle itself. The essential stages usually include data collection, data preparation, model training, evaluation, and deployment. Therefore, data visualization is not an essential lifecycle stage, though it supports understanding and communication.

Option 2: Model Deployment. Deployment is a fundamental phase where a trained model is integrated into a production environment to make predictions on real data. Without deployment, a trained model cannot provide actionable value in practical scenarios. This makes deployment an essential stage.

Option 3: Model Training. Training the model on data to learn parameters is core to any ML pipeline. Without training, there is no model to evaluate or deploy. This is unquestionably an essential stage.

Option 4: Data Collection. Gathering the relevant data is the starting point for any ML project. Without data, there is nothing to train on or evaluate. Thus, data collection is an essential stage.

Summary: Among the listed options, Data Visualization is the one that is not a required core stage of the ML lifecycle, whereas Model Deployment, Model Training, and Data Collection are foundational steps in building and operationalizing machine learning solutions.

Introduction to Artificial Intelligence (082025-VTK)

Which of the following is not an essential stage in the machine learning lifecycle?

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