Google Professional Machine Learning Engineer Question 260
Select 3Google Cloud PlatformYour organization is building a machine learning pipeline to train a large neural network model on a dataset with billions of records. The training process is computationally expensive and requires a distributed training setup across multiple GPUs and nodes. To ensure reliability and scalability of the pipeline, what considerations should you prioritize when designing the distributed training workflow in Google Cloud?
- A
Use Vertex AI Pipelines to orchestrate the entire workflow, ensuring modularity and reusability of pipeline components.
- B
Leverage TensorFlow's tf.distribute.Strategy to handle distribution across multiple GPUs and nodes.
- C
Rely on a single zone for all training resources to minimize network latency during distributed training.
- D
Implement checkpointing to save intermediate model states, enabling recovery in case of failures during training.
- E
Avoid pre-processing data before training to save time and utilize raw data in the training pipeline.
Show answer and explanation
Correct answers: A, B, D
Explanation
To build a reliable and scalable distributed training pipeline on Google Cloud, you need to leverage tools like Vertex AI Pipelines for orchestration, use frameworks like TensorFlow's tf.distribute.Strategy for distributed computation, and implement checkpointing to handle failures gracefully. Avoid relying solely on a single zone for reliability and ensure your data is pre-processed to maintain data quality.
- A. Correct.
Using Vertex AI Pipelines allows you to build modular and reusable workflows, which is critical for reliable and scalable distributed training. It ensures that the pipeline components are well-organized and easy to manage.
- B. Correct.
TensorFlow's tf.distribute.Strategy provides an abstraction for distributed training, enabling you to efficiently distribute computations across multiple GPUs and nodes. This is critical for handling large datasets and models.
- C. Incorrect.
Relying on a single zone can reduce network latency, but it comes at the cost of reliability. If the zone experiences an outage, your entire pipeline could fail. A multi-zone or regional setup is preferred for reliability.
- D. Correct.
Checkpointing is essential for distributed training as it allows the system to recover from failures without needing to restart the entire training process, making the pipeline more reliable.
- E. Incorrect.
Avoiding pre-processing is not recommended. Properly pre-processing data ensures that the model receives clean and consistent inputs, which is crucial for effective training.