Implement Vertex AI Search for retail

You can also directly import your product catalog, but linking to Merchant Center reduces the steps needed to import your catalog. This solution is not ideal if you want to use [facets](facets-overview.md). This turnkey solution works well with Google Ads and is replicated quickly into Vertex AI Search for retail. This can be up and running in a few clicks.

Note that Merchant Center does not support the collections product type. Before importing, make sure to review Merchant Center limitations to check if it meets your catalog needs.

Providing historical user event data lets you to start model training without having to wait months for enough user event data to be collected from your site. To learn how to import user data, refer to the Import user events documentation on importing Google Analytics 360 and 4 events from BigQuery. Your models need sufficient training data before they can provide accurate predictions. To learn how much data to use, understand the requirements for each model.

Set up monitoring and alerts.

A serving config is a serving entity that associates a model and, optionally, controls. These are used like containers when generating your search or recommendation results. When you create a serving config, you can simultaneously create a model (for recommendations only) and controls. You can also create these separately.

If you use recommendations, choose a model type based on the location of your serving config and its objectives. Review the available recommendation types, optimization objectives, and other model tuning options to determine the best options for your business objectives. (For search serving configs, a default model is automatically created.)

Decide if you want to use recommendations, search, or both. Then, familiarize with formats for user events.

If you decide to use search, training and tuning is automatic, assuming you have hit the threshold. Refer to the requirements for user events for each model and each product to determine how many and what type of user events to train and tune the models on.

If you are using recommendations, creating a model initiates training and tuning. Initial model training and tuning takes 2-5 days to complete, but can take longer for large datasets. Initial model training and tuning takes 2-5 days to complete, but can take longer for large datasets.

After your model has been activated, preview and test your serving config's recommendations or search results to ensure your setup is functioning as expected. You can create new controls or use existing controls to add new serving configs and point the application to the test version to compare performance. You can exclude or include rules and split test the production versus another test serving config. You can then simulate searches using these variations in the Evaluations page of the console.

You can use an A/B experiment to compare the performance of your website with and without Vertex AI Search for retail.

Assess the metrics provided by the Search for Retail to help you determine how your business is affected by incorporating Vertex AI Search for retail.

View the metrics for your project on the Analytics page of the Search for Retail console.

Create a Google Cloud project and create authentication credentials including an API key and an OAuth token (either using a user account or a service account) to access the project.

You can add items to your product catalog individually by using the Products.create method. For large product catalogs, we recommend that you add items in bulk using the Products.import method. This provides more configurability and is a good option for businesses who want to want to pilot.

User events track user actions such as clicking on a product, adding an item to a shopping cart, or purchasing an item. User event data is needed to generate personalized results. User events need to be ingested in real time to accurately reflect the behavior of your users.

You can start recording user events in parallel to the catalog import. After the catalog import is complete, rejoin any events that were uploaded before the import completed. You will need to write a tracking pixel.

Providing historical user event data lets you to start model training without having to wait months for enough user event data to be collected from your site. To learn how to import user data, refer to the Import user events documentation on importing events from Cloud Storage, BigQuery or to import events inline using the userEvents.import method. Your models need sufficient training data before they can provide accurate predictions. Then, learn more about the import requirements for each model type.

Set up monitoring and alerts.

A serving config is a serving entity that associates settings with a model and, optionally, controls. These are used when generating your search or recommendation results.

When you create a serving config, you can simultaneously create a model and controls, or create them separately. They offer you a high level of configurability.

If you use recommendations, choose a model type based on the location of your serving config and its objectives. Review the available recommendation types, optimization objectives, and other model tuning options to determine the best options for your business objectives.

Decide if you want to use recommendations, search, or both. Then, familiarize with formats for user events.

If you decide to use search, training and tuning is automatic, assuming you have hit the threshold. Refer to the requirements for user events for each model and each product to determine how many and what type of user events to train and tune the models on.

If you are using recommendations, creating a model initiates training and tuning. Initial model training and tuning takes 2-5 days to complete, but can take longer for large datasets. Initial model training and tuning takes 2-5 days to complete, but can take longer for large datasets.

After your configuration has been activated, preview your serving config's recommendations or search results to ensure your setup is functioning as expected. You will have to use this to create your own custom model. You can create new controls or use existing controls to add new serving configs and point the application to the test version to compare performance. Add or remove rules on the test version to find out which rules may be impacting performance. You can exclude or include rules and split test the production versus another test serving config. You can then simulate searches using these variations in the Evaluations page of the console.

You can use an A/B experiment to compare the performance of your website with and without Vertex AI Search for retail.

Assess the metrics provided by the Search for Retail console to help you determine how your business is affected by incorporating Vertex AI Search for retail.

View the metrics for your project on the Analytics page of the Search for Retail console.