Exam Professional Machine Learning Engineer topic 1 question 258 discussion

The answer is C, to use Translation Hub 1.Accuracy maximization: AutoML Translation uses machine learning to train a translation model on your specific data, which can lead to higher accuracy compared to generic translation models. 2.Minimal operational overhead: AutoML Translation handles the training and deployment of the translation model, reducing the need for manual intervention. 3.Evaluation and correction: The solution includes human reviewers to evaluate and correct any incorrect translations, ensuring high quality.

You want to minimize operational overhead.

Using AutoML Translation (Option C) allows you to train a model on your data, which can be used for translation. You can then configure a Translation Hub project to manage the translation process and use human reviewers to evaluate any incorrect translations. It is scalable solution that maximizes accuracy and minimizes operational overhead.

if we assume there is training data available (source-target language pairs) then I would go with C.

Option A: Cloud Functions are suitable for simple tasks. This approach wouldn't leverage machine learning for improved translations and lacks features like model evaluation and retraining. Option B: Vertex AI pipelines with AutoML Translation training can be powerful, but it might be overkill for this scenario. Additionally, retraining based on a predetermined data skew might not be necessary if human review is effective at catching and correcting errors. Option D: While fine-tuning a pre-trained model with Vertex AI custom training offers flexibility, it requires more expertise and ongoing maintenance compared to the simpler approach of using AutoML Translation.

Answer A It is the only option that makes sense all over. I would go for C if the first sentence was not there "Use AutoML Translation". you can't use autoML because there is no training data.

C: Use AutoML Translation with Translation Hub. Here's why: 1. Scalability: - AutoML Translation: This simplifies model training without extensive manual configuration. - Translation Hub: Centrally stores and manages your translation models, facilitating deployment and reuse across various applications, promoting scalability for your 15 target languages. 2. Accuracy and Evaluation: - AutoML Translation: while pre-trained models might not be perfect, AutoML Translation lets you fine-tune the model with your specific scientific domain data (instruction manuals) to improve accuracy. - Human Review and Iteration: This allows for evaluation and correction of any inaccurate translations, improving overall quality. This is crucial for technical documents like instruction manuals.

Translation Hub can manage translation workloads at scale and also integrate human feedback where required.

So what is the deal? pikachu007 authors the question, adds C as suggested answer and then vote for B?

Agree with pikachu007, I think there is no point in using ML once the manual(human) mode is added.

Translation Hub is a service that allows you to manage and automate your translation workflows on Google Cloud. You can use Translation Hub to upload the documents to a Cloud Storage bucket, select the source and target languages, and apply the trained model to translate the documents. You can use human reviewers to improve the quality and accuracy of the translations, and provide feedback to the ML model.

Option A: While Cloud Functions provide automation, the Cloud Translation API uses generic models that might not be as accurate for domain-specific content, potentially leading to more human corrections. Option C: Translation Hub offers collaboration features but lacks automated model training and pipeline orchestration, requiring more manual effort. Option D: Vertex AI custom training jobs provide flexibility but require more expertise and effort compared to AutoML Translation, and the pre-trained model might not be as well-suited for the specific domain.