Cómo exportar modelos hechos en BQML

How is model export integrated with BigQuery ML?

Exporting a model created in BigQuery ML is crucial to maximize the leverage of the work invested in the development of machine learning models. This process not only ensures that the effort is useful, but also gives different areas of an organization, such as marketing or media, the ability to use the model to optimize resources such as advertising budgets.

Why is model exporting important?

Exporting models is essential because:

• It allows developed solutions to be easily integrated into a company's daily operations.
• It significantly improves business decisions by enabling optimized use of resources.
• It gives access to areas such as marketing to improve the efficiency of your advertising budgets, increasing ROI and promoting a strategic focus on key customer segments.

What are the options for exporting models in BigQuery ML?

BigQuery ML offers several ways to export a model:

1. Google Cloud Console: a visual and straightforward option for exporting models.
2. bq extract command in the command line: Allows the export of the model in a simple and adaptable way.
3. BigQuery API: Ideal for integration into an automated machine learning pipeline.

The choice of method will depend on specific needs, such as pipeline automation.

What considerations should be taken into account when exporting?

It is vital to take certain aspects into account when preparing your models for export:

• Model Formats: each type of model may require a different export format. For example, a DNN (deep neural network) model will be exported in a TensorFlow format.
• Data Structure: Make sure that the input data used during predictions have the same column structure as the data used during training. The use of new attributes that were not present at the start is not supported.
• Model Size Limitations: There is a size limit of 1 GB for matrix factorization models. It is important to take this into account in order not to exceed the allowed size.
• Models that do not support online prediction: Some models, such as AutoML Regressor and AutoML Classifier, are designed for batch processes, not supporting online implementation from the AI platform.

These aspects help optimize the process and ensure that the export runs smoothly and efficiently.

With these tips in mind, you will be ready to export your models and bring real added value to your organization! Keep learning and exploring the potential of BigQuery ML!