https://towardsdatascience.com/time-series-nested-cross-validation-76adba623eb9

The best option to minimize storage and computational overhead is to use the TRANSFORM clause in the CREATE MODEL statement in the SQL query to calculate the required statistics. The TRANSFORM clause allows you to specify feature preprocessinglogic that applies to both training and prediction. The preprocessing logic is executed in the same query as the model creation, which avoids the need to create and store intermediate tables. The TRANSFORM clause also supports quantile bucketization and MinMax scaling, which are the preprocessing steps required for this scenario. Option A is incorrect because creating a component in the Vertex AI Pipelines DAG to calculate the required statistics may increase the computational overhead, as the component needs to run separately from the model creation. Moreover, the component needs to pass the statistics to subsequent components, which may increase the storage overhead. Option B is incorrect because preprocessing and staging the data in BigQuery prior to feeding it to the model may also increase the storage and computational overhead, as you need to create and maintain additional tables for the preprocessed data. Moreover, you need to ensure that the preprocessing logic is consistent for both training and inference. Option C is incorrect because creating SQL queries to calculate and store the required statistics in separate BigQuery tables may also increase the storage and computational overhead, as you need to create and maintain additional tables for the statistics. Moreover, you need to ensure that the statistics are updated regularly to reflect the new data. References:
* BigQuery ML documentation
* Using the TRANSFORM clause
* Feature preprocessing with BigQuery ML

The best approach to build a model that predicts how much inventory the logistics team should order each month is to use a time series forecasting model to predict each item's monthly sales. This approach can capture the temporal patterns and trends in the sales data, such as seasonality, cyclicality, and autocorrelation.
It can also account for the variability and uncertainty in the demand, and provide confidence intervals and error metrics for the predictions. By using a time series forecasting model, you can provide the logistics team with accurate and reliable estimates of the future sales for each item, which can help them optimize the inventory levels and avoid overstocking or understocking. You can use various methods and tools to build a time series forecasting model, such as ARIMA, LSTM, Prophet, or BigQuery ML.
The other options are not optimal for the following reasons:
* A. Using a clustering algorithm to group popular items together is not a good approach, as it does not provide any quantitative or temporal information about the sales or the inventory. It only provides a qualitative and static categorization of the items based on their similarity or dissimilarity. Moreover, clustering is an unsupervised learning technique, which does not use any target variable or feedback to guide the learning process. This can result in arbitrary and inconsistent clusters, which may not reflect the true demand or preferences of the customers.
* B. Using a regression model to predict how much additional inventory should be purchased each month is not a good approach, as it does not account for the individual differences and dynamics of each item.
It only provides a single aggregated value for the whole inventory, which can be misleading and inaccurate. Moreover, a regression model is not well-suited for handling time series data, as it assumes that the data points are independent and identically distributed, which is not the case for sales data. A regression model can also suffer from overfitting or underfitting, depending on the choice and complexity of the features and the model.
* D. Using a classification model to classify inventory levels as UNDER_STOCKED, OVER_STOCKED, and CORRECTLY_STOCKED is not a good approach, as it does not provide any numerical or predictive information about the sales or the inventory. It only provides a discrete and subjective label for the inventory levels, which can be vague and ambiguous. Moreover, a classification model is not well-suited for handling time series data, as it assumes that the data points are independent and identically distributed, which is not the case for sales data. A classification model can also suffer from class imbalance, misclassification, or overfitting, depending on the choice and complexity of the features, the model, and the threshold.
References:
* Professional ML Engineer Exam Guide
* Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate
* Google Cloud launches machine learning engineer certification
* Time Series Forecasting: Principles and Practice
* BigQuery ML: Time series analysis

Vertex AI is a unified platform for building and deploying ML models on Google Cloud. It supports both custom and AutoML models, and provides various tools and services for ML development, such as Vertex Pipelines, Vertex Vizier, Vertex Explainable AI, and Vertex Feature Store. Vertex AI can be used to create models for predicting ticket priority and resolution time, as these are domain-specific tasks that require custom training data and evaluation metrics. Cloud Natural Language API is a pre-trained service that provides natural language understanding capabilities, such as sentiment analysis, entity analysis, syntax analysis, and content classification. Cloud Natural Language API can be used to perform sentiment analysis on the support tickets, as this is a general task that does not require domain-specific knowledge or jargon. The other options are not suitable for the given architecture. AutoML Natural Language and AutoML Vision are services that allow users to create custom natural language and vision models using their own data and labels.
They are not needed for sentiment analysis, as Cloud Natural Language API already provides this functionality. Cloud Vision API is a pre-trained service that provides image analysis capabilities, such as object detection, face detection, text detection, and image labeling. It is not relevant for the support tickets, as they are not expected to have any images. References:
* Vertex AI documentation
* Cloud Natural Language API documentation