You need to build classification workflows over several structured datasets currently stored in BigQuery. Because you will be performing the classification several times, you want to complete the following steps without writing code: exploratory data analysis, feature selection, model building, training, and hyperparameter tuning and serving. What should you do?

Your data science team needs to rapidly experiment with various features, model architectures, and hyperparameters. They need to track the accuracy metrics for various experiments and use an API to query the metrics over time. What should they use to track and report their experiments while minimizing manual effort?

You are a data analyst on a marketing team. You have 40 GB of customer transaction data already stored in BigQuery, and you need to produce a churn-propensity model quickly to validate whether the project is worth further investment. Your team has strong SQL skills but limited Python experience. You want to minimize infrastructure setup. What should you do?

You work for a magazine distributor and need to build a model that predicts which customers will renew their subscriptions for the upcoming year. Using your company's historical data as your training set, you created a TensorFlow model and deployed it to Vertex AI. You need to determine which customer attribute has the most predictive power for each prediction served by the model. What should you do?

You work for a ride-sharing company. Your team trains a demand-forecasting model in a batch pipeline using aggregated features such as 30-minute rolling trip counts per zone. The same features must be served to an online endpoint with single-digit millisecond latency. During production testing, you notice the online predictions differ significantly from offline evaluation results. You need to eliminate the discrepancy while minimizing the amount of code you maintain.
What should you do?