A data scientist needs more compute to create labeled datasets from large data in Google Cloud Storage and Cassandra, plus visualizations. Which solution helps her most?

Deploying Google Cloud Datalab to a VM on Google Compute Engine is the most comprehensive solution. Datalab provides an interactive notebook environment (based on Jupyter) that integrates directly with Google Cloud services like Cloud Storage and BigQuery, making it ideal for data exploration, transformation, and visualization. It allows the data scientist to leverage the scalable compute resources of Compute Engine for processing large datasets and creating labeled data. Running a local Jupyter version won't provide the necessary scalable compute for large datasets or direct, optimized integration with cloud services. Granting Cloud Shell access offers a command-line environment but lacks the interactive notebook and visualization capabilities needed. Hosting a visualization tool on a VM addresses only the visualization aspect, not the data processing and labeling requirements for large datasets.