Google Professional Machine Learning Engineer Question 179
Select 3Google Cloud PlatformYou are a Machine Learning Engineer at a retail company tasked with building a product recommendation system. Your team wants to leverage pre-trained models to accelerate development while ensuring flexibility for finetuning to better align with your customer data. You decide to explore Google Cloud's Model Garden. Which of the following actions are appropriate for your use case?
- A
Use a foundational model available in Model Garden to directly deploy it as a recommendation system without customization.
- B
Finetune an open-source model in Model Garden using your company’s customer interaction data to improve recommendation accuracy.
- C
Leverage a foundational model from Model Garden as a feature extractor and build a custom recommendation pipeline.
- D
Use a pre-trained model in Model Garden without evaluating its suitability for your specific data and use case.
- E
Explore Model Garden’s documentation and examples to identify the best model for your recommendation system.
Show answer and explanation
Correct answers: B, C, E
Explanation
Google Cloud's Model Garden provides access to a variety of foundational and open-source models that can accelerate machine learning workflows. For a recommendation system, it is important to customize or finetune pre-trained models to align with specific business needs. Evaluating the suitability of a model and leveraging Model Garden's resources, such as documentation and examples, are critical steps to ensure the chosen model fits the use case. Finetuning or using a model as a feature extractor are both effective strategies for leveraging pre-trained models while maintaining flexibility.
- A. Incorrect.
Using a foundational model directly without customization might not yield optimal results because it is unlikely to align perfectly with your specific data and use case.
- B. Correct.
This is a correct approach. Finetuning a pre-trained model with your own data ensures the model is tailored to your business needs, improving performance on your specific recommendation task.
- C. Correct.
This is a valid approach. Using a pre-trained model as a feature extractor allows you to leverage its learned representations while building a custom pipeline for your specific use case.
- D. Incorrect.
Using a pre-trained model without evaluating its suitability is not a recommended practice as it may lead to suboptimal results or even incorrect recommendations.
- E. Correct.
Exploring Model Garden’s documentation and examples helps you identify the most appropriate model for your use case, aligning with best practices.