How to train my model with my own data?

[LeoDev1]

I aim to train my model exclusively by providing information about my BigQuery (BQ) structure, without relying on specific examples of input and output. How can I do that?

[UtsavS26]

While it's not possible to train a model solely on BQ structure without any input-output examples, there are approaches that leverage BQ information for enhanced training:

1. BigQuery ML (BQML):

• Train models directly within BigQuery: Streamlines model training, evaluation, and prediction using SQL-like syntax.
• Leverage BQ schema for feature definitions: Automatically infers features and data types from table schema, reducing explicit feature engineering.
• Access data through SQL queries: No need to extract data, enabling model training on large datasets.

2. Integrate BQ with External ML Frameworks:

• Query data from BQ into frameworks: Use tools like TensorFlow, PyTorch, or scikit-learn to train models with data retrieved from BQ.
• Utilize BQ metadata for feature engineering: Leverage table and column descriptions for feature creation and selection.
• Combine BQ data with external sources: Fuse BQ information with other data sources for richer model training.

3. Zero-Shot and Few-Shot Learning Techniques:

• Explore zero-shot or few-shot learning models: These can learn tasks without extensive labeled examples, potentially reducing reliance on explicit input-output pairs.
• Combine with BQ knowledge bases or ontologies: Integrate structural information from BQ to enhance model understanding.