Exam Professional Machine Learning Engineer topic 1 question 219 discussion

My answer: D This Google Documentation explains “Instead of deploying the model to an endpoint, you can use the RunInference API to serve machine learning models in your Apache Beam pipeline. This approach has several advantages, including flexibility and portability.” https://cloud.google.com/blog/products/ai-machine-learning/streaming-prediction-with-dataflow-and-vertex This documentation uses RunInference and WatchFilePattern to “to automatically update the ML model without stopping the Apache Beam”. https://cloud.google.com/dataflow/docs/notebooks/automatic_model_refresh So, thinking in “minimize prediction latency”, its suggested use RunInfenrece, while “effort required to update the model” the **WatchFilePattern is the best approach.** I think D is the best option

C) Expose the model as Vertex AI End Point

agree with guilhermebutzke

A for me.

Automatic Model Updates: WatchFilePattern automatically detects model changes in Cloud Storage, leading to seamless updates without managing endpoint deployments.

Low Latency: Serverless Execution: Cloud Functions start up almost instantly, reducing prediction latency compared to alternatives that require longer setup or deployment times. In-Memory Model: Loading the model into memory eliminates disk I/O overhead, further contributing to rapid predictions.