You are developing an ML pipeline which consists of several steps.
In the chain of steps you need to pass the data from Step1 to Step2, for further processing.
You are using Python SDK.How can you achieve this goal?
Click on the arrows to vote for the correct answer
A. B. C. D.Answer: B.
Option A is incorrect because you can use datasets for data that are available persistently all over the ML workspace.
For intermediate data moving between pipeline steps, use the PipelineData object.
Option B is CORRECT because PipelineData objects exist beyond single pipeline steps, they must be defined in the pipeline definition script.
In an ML pipeline, PipelineData object is designed to be used for passing the resulting temporary data from one step to another.
Option C is incorrect because PipelineData objects exist beyond single pipeline steps, so they have to be defined in the pipeline definition script.
Option D is incorrect because PipelineParameter object is used to pass model parameters for pipeline runs.
It's not appropriate for passing datasets.
Reference:
In order to pass data from one step to another in an ML pipeline developed using Python SDK, you have different options available. The best option depends on the specific scenario and requirements.
The possible options are:
A. Pass a Dataset object as an argument from Step1 to Step2: In this option, you pass the dataset object from Step1 to Step2 as an argument. This is a straightforward approach, but it can become cumbersome when there are many steps involved in the pipeline, and the data needs to be passed through all of them.
B. Define a PipelineData object in the pipeline definition script and use it as“outputs=” and “inputs=”, respectively: In this option, you define a PipelineData object in the pipeline definition script, and then use it to pass the data between the steps. This is a recommended approach as it allows you to define a data dependency between the steps, which can help with the pipeline orchestration and debugging.
C. Define a PipelineData object in Step1 and pass it to Step2 as“outputs=” and “inputs=”, respectively: This option is similar to the previous one, but instead of defining the PipelineData object in the pipeline definition script, you define it in Step1 and pass it to Step2. This can be useful when you want to reuse the same data in multiple steps or when you have complex data processing requirements in Step1.
D. Define a PipelineParameter and use it to pass the dataset from Step1 to Step2: In this option, you define a PipelineParameter object in the pipeline definition script, which represents a placeholder for the dataset. Then, you pass the PipelineParameter object from Step1 to Step2, and set its value to the actual dataset. This can be useful when you want to decouple the data from the pipeline definition and make it more flexible.
In summary, the best option to pass data from one step to another in an ML pipeline depends on the specific requirements and constraints. However, using PipelineData objects is a recommended approach as it allows you to define data dependencies between the steps, which can help with pipeline orchestration and debugging.