Google Professional Machine Learning Engineer Question 278
Select 3Google Cloud PlatformYou are a machine learning engineer working for a retail company that wants to use a foundational model to improve its product recommendation system. The team has identified a pre-trained text generation model from Model Garden on Vertex AI. You need to fine-tune this model to better align with your specific domain data, which includes customer reviews and product descriptions. Which steps should you take to fine-tune the foundational model on Vertex AI?
- A
Prepare a labeled dataset of customer reviews and product descriptions, and upload it to a Google Cloud Storage bucket.
- B
Use the Vertex AI Training feature to train the foundational model from scratch using the domain-specific data.
- C
Use the pre-trained foundational model from Model Garden and configure a custom training job in Vertex AI with your domain-specific data.
- D
Leverage Vertex AI TensorBoard to monitor the fine-tuning process in real time.
- E
Evaluate the fine-tuned model using a validation dataset to ensure it meets performance requirements.
Show answer and explanation
Correct answers: A, C, E
Explanation
Fine-tuning foundational models in Vertex AI involves using pre-trained models available in Model Garden and adapting them to specific tasks by using domain-specific data. The process requires preparing a labeled dataset, configuring a custom training job to fine-tune the model, and evaluating the model with a validation dataset. Training from scratch is not necessary, as foundational models are pre-trained to save time and computational resources. Monitoring tools like TensorBoard are helpful but optional.
- A. Correct.
Preparing a labeled dataset is essential for fine-tuning any model, as it provides the domain-specific data required to adapt the pre-trained foundational model to the new task.
- B. Incorrect.
Training the model from scratch is unnecessary when using a foundational model, as these models are pre-trained and meant to be fine-tuned rather than rebuilt entirely.
- C. Correct.
Using a pre-trained foundational model and configuring a custom training job in Vertex AI is the correct approach to fine-tune the model with your specific data.
- D. Incorrect.
While Vertex AI TensorBoard can be useful for monitoring training jobs, it is not a mandatory step for fine-tuning foundational models.
- E. Correct.
Evaluating the fine-tuned model with a validation dataset is crucial to ensure it meets the performance requirements for the intended use case.