What is the first key step in creating a recommendation system with BigQuery ML?

Preparing the training data in BigQuery is the foundational step in creating a recommendation system with BigQuery ML. This process involves gathering, cleaning, and structuring the data so that it accurately represents user interactions, product attributes, and any other pertinent features that will inform the model.

Effective preparation of the training data includes ensuring that the data is well-organized, free of biases, and contains enough relevant information that captures user preferences and behaviors. This stage is crucial because the effectiveness of the recommendation system heavily relies on the quality and the relevance of the training data.

Once this step is completed, you can then move on to training the model. However, without properly prepared data, any subsequent steps would lack the necessary foundation for the model to learn from, leading to potentially ineffective recommendations in production.