PROFESSIONAL-MACHINE-LEARNING-ENGINEER · Question #310
Your team is experimenting with developing smaller, distilled LLMs for a specific domain. You have performed batch inference on a dataset by using several variations of your distilled LLMs and stored
The correct answer is C. Create a custom Vertex AI Pipelines component that reads the batch inference outputs from Cloud. Vertex AI Pipelines supports custom components that integrate with your existing workflows and allow you to track artifacts such as evaluation metrics, outputs, and metadata. By creating a custom component, you can tailor the evaluation process to your specific needs. This approa
Question
Options
- ADevelop a custom Python component that reads the batch inference outputs from Cloud Storage,
- BUse a Dataflow component that processes the batch inference outputs from Cloud Storage,
- CCreate a custom Vertex AI Pipelines component that reads the batch inference outputs from Cloud
- DUse the Automatic side-by-side (AutoSxS) pipeline component that processes the batch inference
How the community answered
(54 responses)- A17% (9)
- B7% (4)
- C72% (39)
- D4% (2)
Explanation
Vertex AI Pipelines supports custom components that integrate with your existing workflows and allow you to track artifacts such as evaluation metrics, outputs, and metadata. By creating a custom component, you can tailor the evaluation process to your specific needs. This approach leverages Vertex AI’s built-in tracking and metadata capabilities to maintain an auditable record of model performance, ensuring reproducibility and comparability across LLM variations.
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