The best way to operationalize your training process is to use Vertex AI Pipelines, which allows you to create and run scalable, portable, and reproducible workflows for your ML models. Vertex AI Pipelines also integrates with Vertex AI Metadata, which tracks the provenance, lineage, and artifacts of your ML models.
By using a Vertex AI CustomTrainingJobOp component, you can train your model using the same code as in your Jupyter notebook. By using a ModelUploadOp component, you can upload your trained model to Vertex AI Model Registry, which manages the versions and endpoints of your models. By using Cloud Scheduler and Cloud Functions, you can trigger your Vertex AI pipeline to run weekly, according to your plan. References:
* Vertex AI Pipelines documentation
* Vertex AI Metadata documentation
* Vertex AI CustomTrainingJobOp documentation
* ModelUploadOp documentation
* Cloud Scheduler documentation
* [Cloud Functions documentation]

* Option A is correct because using Vertex AI Workbench user-managed notebooks to generate the report is the best way to quickly determine whether the data is suitable for model development, and to create a one-time report that includes both informative visualizations of data distributions and more sophisticated statistical analyses to share with other ML engineers on your team. Vertex AI Workbench is a service that allows you to create and use notebooks for ML development and experimentation. You can use Vertex AI Workbench to connect to your BigQuery table, query and analyze the data using SQL or Python, and create interactive charts and plots using libraries such as pandas, matplotlib, or seaborn.
You can also use Vertex AI Workbench to perform more advanced data analysis, such as outlier detection, feature engineering, or hypothesis testing, using libraries such as TensorFlow Data Validation, TensorFlow Transform, or SciPy. You can export your notebook as a PDF or HTML file, and share it with your team. Vertex AI Workbench provides maximum flexibility to create your report, as you can use any code or library that you want, and customize the report as you wish.
* Option B is incorrect because using Google Data Studio to create the report is not the most flexible way to quickly determine whether the data is suitable for model development, and to create a one-time report that includes both informative visualizations of data distributions and more sophisticated statistical analyses to share with other ML engineers on your team. Google Data Studio is a service that allows you to create and share interactive dashboards and reports using data from various sources, such as BigQuery, Google Sheets, or Google Analytics. You can use Google Data Studio to connect to your BigQuery table, explore and visualize the data using charts, tables, or maps, and apply filters, calculations, or aggregations to the data. However, Google Data Studio does not support more sophisticated statistical analyses, such as outlier detection, feature engineering, or hypothesis testing, which may be useful for model development. Moreover, Google Data Studio is more suitable for creating recurring reports that need to be updated frequently, rather than one-time reports that are static.
* Option C is incorrect because using the output from TensorFlow Data Validation on Dataflow to generate the report is not the most efficient way to quickly determine whether the data is suitable for model development, and to create a one-time report that includes both informative visualizations of data distributions and more sophisticated statistical analyses to share with other ML engineers on your team.
TensorFlow Data Validation is a library that allows you to explore, validate, and monitor the quality of your data for ML. You can use TensorFlow Data Validation to compute descriptive statistics, detect anomalies, infer schemas, and generate data visualizations for your data. Dataflow is a service that allows you to create and run scalable data processing pipelines using Apache Beam. You can use Dataflow to run TensorFlow Data Validation on large datasets, such as those stored in BigQuery.
However, this option is not very efficient, as it involves moving the data from BigQuery to Dataflow, creating and running the pipeline, and exporting the results. Moreover, this option does not provide maximum flexibility to create your report, as you are limited by the functionalities of TensorFlow Data Validation, and you may not be able to customize the report as you wish.
* Option D is incorrect because using Dataprep to create the report is not the most flexible way to quickly determine whether the data is suitable for model development, and to create a one-time report that includes both informative visualizations of data distributions and more sophisticated statistical analyses to share with other ML engineers on your team. Dataprep is a service that allows you to explore, clean, and transform your data for analysis or ML. You can use Dataprep to connect to your BigQuery table, inspect and profile the data using histograms, charts, or summary statistics, and apply transformations, such as filtering, joining, splitting, or aggregating, to the data. However, Dataprep does not support more
* sophisticated statistical analyses, such as outlier detection, feature engineering, or hypothesis testing, which may be useful for model development. Moreover, Dataprep is more suitable for creating data preparation workflows that need to be executed repeatedly, rather than one-time reports that are static.
References:
* Vertex AI Workbench documentation
* Google Data Studio documentation
* TensorFlow Data Validation documentation
* Dataflow documentation
* Dataprep documentation
* [BigQuery documentation]
* [pandas documentation]
* [matplotlib documentation]
* [seaborn documentation]
* [TensorFlow Transform documentation]
* [SciPy documentation]
* [Apache Beam documentation]

* A fairness metric is a way to measure how well a machine learning model treats different groups of customers, such as by sex or age. A common fairness metric is accuracy, which is the proportion of correct predictions among all predictions. Accuracy across the sensitive features means calculating the accuracy for each group separately, and then comparing them. For example, if the model has 90% accuracy for male customers and 80% accuracy for female customers, there is a 10% accuracy gap that indicates potential bias against female customers.
* To investigate and mitigate potential bias, it is important to define a fairness metric and evaluate it on a test set. A test set is a subset of the data that is not used for training the model, but only for evaluating its performance. By joining the test set predictions with the sensitive features, you can calculate the fairness metric and see if it meets your requirements. For example, you may require that the accuracy gap between any two groups is less than 5%. If the fairness metric does not meet your requirements, you may need to adjust the model or the data to reduce bias.
* Option A is not the best answer because excluding the sensitive features and any meaningfully correlated features may not eliminate bias. For example, if salary is correlated with sex, and salary is also a predictive feature for the target variable, excluding both features may reduce the model accuracy and still leave some residual bias. Moreover, excluding features based on correlation may not capture the complex interactions and dependencies among the features that may affect bias.
* Option B is not the best answer because using the global attribution values for each feature of the model may not reflect the individual-level impact of the features on the predictions. Global attribution values are calculated by averaging the attribution values across all the data points, and they indicate how important each feature is for the overall model performance. However, they do not show how each
* feature affects each customer's prediction, which may vary depending on the values of the other features. For example, sex may have a low global attribution value, but it may have a high impact on some customers' predictions, especially if it interacts with other features such as salary or age.
* Option C is not the best answer because discarding the model and training the model again without a feature based on a single customer's attribution value may not be a robust or scalable way to mitigate bias. Attribution values are calculated by measuring how much each feature contributes to the prediction for a given data point, and they indicate how sensitive the prediction is to the feature value. However, they do not show how the feature affects the overall fairness metric or the model accuracy. For example, sex may have a high attribution value for a customer, but it may not affect the accuracy gap between the groups. Moreover, discarding and retraining the model based on a single customer's attribution value may not be feasible if there are many customers with high attribution values for different features.

A feature cross is a synthetic feature that is obtained by combining two or more existing features, usually by taking their product or concatenation. A feature cross can help to capture the nonlinear and interaction effects between the original features, and improve the predictive performance of themodel. A feature cross can be applied to different types of features, such as numeric, categorical, or geospatial features1.
For the use case of building an ML model to predict car sales in different cities around the world, the best option is to use one feature obtained as an element-wise product between binned latitude, binned longitude, and one-hot encoded car type. This option involves creating a feature cross that combines three individual features: binned latitude, binned longitude, and one-hot encoded car type. Binning is a technique that transforms a continuous numeric feature into a discrete categorical feature by dividing its range into equal intervals, or bins. One-hot encoding is a technique that transforms a categorical feature into a binary vector, where each element corresponds to a possible category, and has a value of 1 if the feature belongs to that category, and 0 otherwise. By applying binning and one-hot encoding to the latitude, longitude, and car type features, the feature cross can capture the city-specific relationships between car type and number of sales, as each combination of bins and car types can represent a different city and its preference for a certain car type.
For example, the feature cross can learn that a city with a latitude bin of [40, 50], a longitude bin of [-80, -70], and a car type of SUV has a higher number of sales than a city with a latitude bin of [-10, 0], a longitude bin of
[10, 20], and a car type of sedan. Therefore, using one feature obtained as an element-wise product between binned latitude, binned longitude, and one-hot encoded car type is the best option for this use case.
References:
* Feature Crosses | Machine Learning Crash Course