Focusing solely on the development of high-accuracy ML models to improve product recommendations, ignoring the infrastructure scalability.

Prioritizing the collection of vast amounts of customer data without establishing a clear data governance or processing strategy, assuming more data will inherently lead to better models.

Implementing a comprehensive model evaluation framework to continuously assess model performance post-deployment, without considering the scalability of the evaluation process itself.

Designing a system that encompasses scalable data pipelines, efficient model training and deployment processes, and the ability to dynamically adjust to increasing data volumes and model complexity, while ensuring data privacy and compliance.