Which stages of the ML workflow can be managed with Vertex AI?

The correct choice emphasizes the comprehensive capabilities of Vertex AI in managing specific stages of the machine learning workflow. Vertex AI is designed to streamline the process of developing, deploying, and maintaining machine learning models, making it a powerful tool for professionals in this field.

By stating that you can create a dataset and upload data, train an ML model, and deploy the trained model to an endpoint for serving predictions, the choice highlights critical tasks within the machine learning lifecycle.

Creating datasets and uploading data is foundational for any ML project, as the quality and availability of data directly affect model performance. Training an ML model is another key stage where algorithms learn from data to find patterns. Subsequently, deploying the trained model to an endpoint is crucial for making predictions available in a production environment, enabling real-time decision-making and application of insights.

While other options may involve important tasks in the machine learning workflow, they do not encompass the same breadth of functionality that Vertex AI offers, particularly regarding end-to-end management of the data lifecycle to model deployment. This end-to-end capability is what makes option B the most representative of Vertex AI's strengths in managing the ML workflow.