You work for a company that sells corporate electronic products to thousands of businesses worldwide. Your company stores historical customer data in BigQuery. You need to build a model that predicts customer lifetime value over the next three years. You want to use the simplest approach to build the model and you want to have access to visualization tools. What should you do?
To build a model predicting customer lifetime value using the simplest approach while having access to visualization tools, using the BigQuery console to directly create the model with the CREATE MODEL statement is the best option. This approach requires fewer steps and complexity compared to creating new datasets or exporting and re-importing data. The BigQuery console additionally allows for the use of ML.EVALUATE and ML.PREDICT statements to validate the results, streamlining the entire process within one interface.
Option B because there's no mention of "flexibility". Easy access to viz tools with Looker
B. Use Bigquery ML Features to create, evaluate and predict
D Vertex AI Workbench notebook: Provides an environment for data analysis, model building, and visualization tools all in one place. IPython magics: Allows seamless interaction with BigQuery for data exploration and feature creation directly within the notebook. CREATE MODEL statement: Enables model creation within the notebook environment, simplifying the workflow. ML.EVALUATE and ML.PREDICT statements: Facilitate model validation directly within the notebook for assessing performance.
Simplest approach that allows visualization is option B.
As requested :" simplest approach", the option B is the best choice.
As requested :" simplest approach", the option B is the best choice.
Option B: While AutoML simplifies model selection and training, it lacks the flexibility and visualization capabilities of Vertex AI Workbench. Option C: Manually saving features as CSV files and importing them back into BigQuery involves unnecessary data movement and complexity. Option D: Completing all steps within the notebook is possible but requires more coding and might not be as intuitive for those less familiar with BigQuery ML syntax.
all the other options create a new BQ table, I don't think it's needed.
Forgot to vote.