gcloud ml-engine local train - run a Cloud ML Engine training job locally

This command runs the specified module in an environment similar to that of a live Cloud ML Engine Training Job.

This is especially useful in the case of testing distributed models, as it allows you to validate that you are properly interacting with the Cloud ML Engine cluster configuration.

To apply complex business logic on a JSON response using Python's standard library within a Workflow, invoking a Cloud Function is the most efficient and straightforward approach. Here's why option A is the best choice:

Cloud Functions:

Cloud Functions provide a lightweight, serverless execution environment for running code in response to events. They support Python and can easily integrate with Workflows.

This approach ensures simplicity and speed of execution, as Cloud Functions can be invoked directly from a Workflow and handle the complex logic required.

Flexibility and Simplicity:

Using Cloud Functions allows you to leverage Python's extensive standard library and ecosystem, making it easier to implement and maintain the complex business logic.

Cloud Functions abstract the underlying infrastructure, allowing you to focus on the application logic without worrying about server management.

Performance:

Cloud Functions are optimized for fast execution and can handle the processing of the JSON response efficiently.

They are designed to scale automatically based on demand, ensuring that your workflow remains performant.

Steps to Implement:

Write the Cloud Function:

Develop a Cloud Function in Python that processes the JSON response and applies the necessary business logic.

Deploy the function to Google Cloud.

Invoke Cloud Function from Workflow:

Modify your Workflow to call the Cloud Function using an HTTP request or Google Cloud Function connector.

steps:

- callCloudFunction:

call: http.post

args:

url: https://REGION-PROJECT_ID.cloudfunctions.net/FUNCTION_NAME

body:

key: value

Process Results:

Handle the response from the Cloud Function and proceed with the next steps in the Workflow, such as loading data into BigQuery.

Google Cloud Functions Documentation

Using Workflows with Cloud Functions

Workflows Standard Library

Feature Crosses:

Feature crosses combine multiple features into a single feature that captures the interaction between them. For location data, a feature cross of latitude and longitude can capture spatial dependencies that affect housing prices.

This approach allows the neural network to learn complex patterns related to geographic location more effectively than using raw latitude and longitude values.

Numerical Representation:

Converting the feature cross into a numeric column simplifies the input for the neural network and can improve the model's ability to learn from the data.

This method ensures that the model can leverage the combined information from both latitude and longitude in a meaningful way.

Model Training:

Using a numeric column for the feature cross helps in regularizing the model and prevents overfitting, which is crucial for achieving good generalization on unseen data.

To engineer a feature that incorporates the physical dependency of location on housing prices for a neural network, creating a numeric column from a feature cross of latitude and longitude is the most effective approach. Here's why option B is the best choice:

The following rules will apply when you use preemptible workers with a Cloud Dataproc cluster:

. Processing only---Since preemptibles can be reclaimed at any time, preemptible workers do not store data. Preemptibles added to a Cloud Dataproc cluster only function as processing nodes.

. No preemptible-only clusters---To ensure clusters do not lose all workers, Cloud Dataproc cannot create preemptible-only clusters.

. Persistent disk size---As a default, all preemptible workers are created with the smaller of 100GB or the primary worker boot disk size. This disk space is used for local caching of data and is not available through HDFS.

The managed group automatically re-adds workers lost due to reclamation as capacity permits.