Exam Professional Machine Learning Engineer topic 1 question 95 discussion

From: https://cloud.google.com/vertex-ai/docs/pipelines/build-pipeline#sdk "1. If you use TensorFlow in an ML workflow that processes terabytes of structured data or text data, we recommend that you build your pipeline using TFX. To learn more about building a TFX pipeline, follow the TFX getting started tutorials. To learn more about using Vertex AI Pipelines to run a TFX pipeline, follow the TFX on Google Cloud tutorials. 2. For other use cases, we recommend that you build your pipeline using the Kubeflow Pipelines SDK. By building a pipeline with the Kubeflow Pipelines SDK, you can implement your workflow by building custom components or reusing prebuilt components, such as the Google Cloud Pipeline Components. Google Cloud Pipeline Components make it easier to use Vertex AI services like AutoML in your pipeline." So I guess since it is image processing, it should be Kubeflow - answer C (TFX is for structured or text data).

D is can be or not based on the data, C is 100% irrespective of data, therefore C

Vertex AI Pipelines: This is Google Cloud's managed service for orchestrating ML workflows. It handles the execution of your pipeline steps, manages dependencies, and provides monitoring and logging capabilities. Crucially, it minimizes cluster management, which is a key requirement for you. TensorFlow Extended (TFX) SDK: TFX is a powerful SDK specifically designed for building production-ready ML pipelines. It provides pre-built components for common tasks like data ingestion, preprocessing, training, evaluation, and deployment, making it well-suited for your video frame processing and object detection pipeline. Integration with Vertex AI: TFX components are designed to work seamlessly with Vertex AI services, including training jobs and endpoints. This ensures smooth transitions between pipeline steps and simplifies model deployment.

This question is designed to be TFX. It would be a weird thing to do to say "Vertex pipelines with kubeflow SDK" because that's just one of the ways to implement stuff in vertex pipelines, which it doesn't normally specify. TFX adds the things in the question on top of the functionality of Vertex.

minimal cluster management should rule out option c. why has everyone chosen that!it should be b

You have to understand the ML lifecycle and difference between TFX and KFP better here. For ML end to end life cycle TFX is a better option, you can ensure Drift Detection/ Train Serve SKew with TFDV, you can easily perform serving with Tf.Serve and easily integrate TFX with Vertex AI pipeline which runs serverless. All these features are not directly available/managed in KFP ( as its user centric and user managed library). So I would go here with B.

If you use TensorFlow in an ML workflow that processes terabytes of structured data or text data, should use TFX. For other use cases, Kubeflow. Link: https://cloud.google.com/vertex-ai/docs/pipelines/build-pipelin

Overall, using Vertex AI Pipelines with TensorFlow Extended (TFX) SDK provides a comprehensive and managed solution for handling video feed data in an ML pipeline, while minimizing the need for manual infrastructure management and maximizing scalability and efficiency.

I vote for be. the question stated that the minumumn clustering management is required, and I found this on the google study guide" Vertex AI Pipelines automatically provisions underlying infrastructure and managed it for you"

minimal managment

Went with C

Vertex AI Pipelines with Kubeflow Pipelines SDK provides a high-level interface for building end-to-end machine learning pipelines. This approach allows for easy integration with Google Cloud services, including Cloud Storage for data ingestion and preprocessing, Vertex AI for training and hyperparameter tuning, and deployment to an endpoint. The Kubeflow Pipelines SDK also allows for easy orchestration of the entire pipeline, minimizing cluster management.

Answer is C. If you use TensorFlow in an ML workflow that processes terabytes of structured data or text data, should use TFX. For other use cases, Kubeflow. Link: https://cloud.google.com/vertex-ai/docs/pipelines/build-pipeline

Answer C... https://cloud.google.com/architecture/ml-on-gcp-best-practices#use-vertex-pipelines

Google want you to use core native service Pipeline, Don't overthink but , need to think it over. The anwser is in https://cloud.google.com/architecture/architecture-for-mlops-using-tfx-kubeflow-pipelines-and-cloud-build https://cloud.google.com/vertex-ai/docs/pipelines

" You want to orchestrate the entire pipeline with minimal cluster management" because of that it cant be answer c i vote for b, becausse there is no cluster management with vertex ai

C "If you are using other frameworks, we recommend using Kubeflow Pipeline, which is very flexible and allows you to use simple code to construct pipelines. Kubeflow Pipeline also provides Google Cloud pipeline components such as Vertex AI AutoML." (Journey to Become a Google Cloud Machine Learning Engineer: Build the mind and hand of a Google Certified ML professional)