Module Quiz » Week 1 » ML Pipelines On Google Cloud

Q: Which of the following are benefits of running TFX on Google Cloud? Select all that apply.

Correct! Google Cloud’s managed Dataflow service enables scalable, distributed data processing for your TFX pipeline. <br> Correct! Google Cloud development tools like Cloud Functions, Container Registry, and Cloud Build streamline TFX pipeline code sharing, testing, deployment, and continuous training. <br> Correct! Running TFX on Google Cloud enables you to run multiple pipelines in parallel on distributed resources with different sets of data splits, models, and hyperparameters to build and deploy TensorFlow models to production faster.

Q: Which of the following are benefits of incorporating the ExampleValidator component into your ML project lifecycle? Select all that apply.

Correct! ExampleValidator is a key part of a TFX continuous training pipeline that compares data splits against the schema artifact to identify train/serving skew that can negatively impact model performance. <br> Correct! ExampleValidator is a key part of a TFX continuous training pipeline that compares data splits against the schema artifact to identify data anomalies that can negatively impact model performance. <br> Correct! ExampleValidator is a key part of a TFX continuous training pipeline that compares data spans over time against the schema artifact to identify data drift that can negatively impact model performance.