Google Professional Machine Learning Engineer
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As a Google Professional Machine Learning Engineer at a startup utilizing Google Cloud, you are tasked with optimizing the data pipeline for multiple TensorFlow models. The data is in Parquet format, and the team requires a solution that efficiently manages this data both as input and output, without incurring additional infrastructure costs or complexity. The solution must also ensure seamless integration with TensorFlow to maintain development velocity. Considering these constraints, which of the following solutions should you implement? (Choose one correct option)
Explanation:
TensorFlow I/O is the optimal choice because it provides direct support for Parquet files, among other formats, enabling seamless integration with TensorFlow without the need for additional data transformations or infrastructure. This approach aligns with the startup's constraints by minimizing costs and complexity while maintaining efficiency. Alternatives like Dataflow Flex Templates, BigQuery to TFRecords, and Cloud Dataproc introduce unnecessary steps, costs, or infrastructure overhead, making them less suitable for the given scenario. For further reading, consult the TensorFlow I/O documentation and this article on efficient data formats for TensorFlow.