You are developing a batch process that will train a custom model and perform predictions. You need to be able to show lineage for both your model and the batch predictions. What should you do?
A.
1. Upload your dataset to BigQuery. 2. Use a Vertex AI custom training job to train your model. 3. Generate predictions by using Vertex AI SDK custom prediction routines.
B.
1. Use Vertex AI Experiments to evaluate model performance during training. 2. Register your model in Vertex AI Model Registry. 3. Generate batch predictions in Vertex AI.
C.
1. Create a Vertex AI managed dataset. 2. Use a Vertex AI training pipeline to train your model. 3. Generate batch predictions in Vertex AI.
D.
1. Use a Vertex AI Pipelines custom training job component to train your model. 2. Generate predictions by using a Vertex AI Pipelines model batch predict component.
A: Vertex AI SDK custom prediction routines do not provide lineage
B: it focuses more on experiments and does not provide lineage as Vertex AI pipelines
C: might appear correct, especially by the use of managed dataset, but a generic Vertex AI training pipeline does not provide lineage for a custom model as much as a custom training job component of D.
A (Vertex AI custom training job and custom prediction routines): This approach lacks the built-in lineage tracking capabilities of Vertex AI Pipelines. You would need to implement custom mechanisms to log and track the relevant metadata.
B (Vertex AI Experiments and Model Registry): These are valuable tools, but they focus more on experiment management and model versioning. They don't provide the same level of workflow and lineage tracking as pipelines.
C (Vertex AI managed dataset and batch prediction): While helpful, this doesn't provide the same level of granularity and traceability as pipelines for tracking the complete lineage, especially the training process.
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MarcoPellegrino
3 months, 2 weeks agoAB_C
5 months ago