Exam Professional Machine Learning Engineer topic 1 question 193 discussion

My answer: B According to https://cloud.google.com/vertex-ai/docs/text-data/sentiment-analysis/prepare-data, AutoML sentiment analysis requires a minimum of 10 labeled training documents per sentiment category, with a maximum of 100,000 total training documents. This means you need to ensure you have an adequate amount of labeled data to train a reliable model. Therefore, option B is more suitable since the API will return the sentiment and there is no mention of a customized problem that justifies the use of AutoML.

It is definitely A and not B. Natural Language API can be used for sentiment analysis but it will require an additional Cloud Function to handle the sentiment analysis, which adds complexity and overhead. Since AutoML models are built specifically for sentiment analysis tasks, using AutoML directly is more efficient.

Agree with guilhermebutzke

Scalability: Uploading audio files to Cloud Storage provides a scalable and reliable storage solution for your large dataset. Asynchronous Processing: The speech:longrunningrecognize API enables asynchronous transcription, allowing your code to proceed without waiting for each file to finish processing. This improves overall throughput. Managed Service: Cloud Functions are serverless functions that automatically scale to handle the workload. You don't need to manage servers or infrastructure. Natural Language API Integration: The Cloud Function can directly call the Natural Language API's analyzeSentiment method for sentiment analysis, streamlining the workflow.

Agree with guilhermebutzke

A) Efficiency: Option A leverages the optimized and scalable infrastructure of Google Cloud Platform (GCP). Using the speech:longrunningrecognize API allows you to transcribe large audio files efficiently without overwhelming your local machine or network. Cost-effectiveness: Paying for processing in Cloud Storage can be more cost-effective than performing it locally, especially for large datasets. Ease of use: The Cloud Storage and Speech-to-Text APIs are well-documented and provide readily available libraries for easy integration. Scalability: This approach scales easily as your dataset grows, as GCP can handle large workloads efficiently.

Re-considering as question states large dataset going with option A

I’m going with b and agree with BlehMaks - For your convenience, the Natural Language API can perform sentiment analysis directly on a file located in Cloud Storage, without the need to send the contents of the file in the body of your request. Googles best practices try api first then auto ml then custom training. https://cloud.google.com/natural-language/docs/analyzing-sentiment

A. It must be longrunningrecognize -> no C. No point speaking about Python files -> no D. Final question being: NL analyzeSentiment or AutoML sentiment? I feel due to large dataset VertexAI AutoML is the way to go (can scale to large volumes of data)

Vertex AI AutoML is overkill as the build-in NL API provides sentiment analysis.

B Because don't need to train model just use google api transcride and sentiment analysis

Efficient audio processing: speech:longrunningrecognize is specifically designed for handling large audio files, offering asynchronous processing and optimized performance. Scalability: Cloud Storage and Vertex AI AutoML scale seamlessly to handle large volumes of data and model inferences. Cost-effectiveness: Separating transcription and sentiment analysis allows for potential cost optimization by using different pricing models for each service.