Google Professional Machine Learning Engineer
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As the Director of Data Science at a large company, your team has adopted the Kubeflow Pipelines SDK for orchestrating machine learning workflows. They are currently facing integration challenges with their custom Python code into the SDK. The team is under tight deadlines and needs a solution that is both efficient and scalable, without incurring unnecessary costs. Additionally, the solution must ensure that the custom code can be easily maintained and updated. Considering these constraints, what is the most efficient method to integrate their custom Python code with the Kubeflow Pipelines SDK? (Choose one correct option)
A
Deploy the custom Python code to Cloud Functions, and use Kubeflow Pipelines to trigger the Cloud Function. This approach leverages serverless computing to reduce operational overhead.
B
Package the custom Python code into Docker containers, and use the load_component_from_file function to import the containers into the pipeline. This method provides isolation and reproducibility but requires additional container management._
C
Use the predefined components available in the Kubeflow Pipelines SDK to access Dataproc, and run the custom code there. This option utilizes managed services for ease of use but may introduce latency and cost concerns.
D
Use the func_to_container_op function to create custom components from the Python code. This function simplifies the process by automatically generating a Docker container with all necessary dependencies, facilitating seamless integration with the Kubeflow Pipelines SDK._