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

Vertex AI endpoints are built for online prediction and automatically serve updated models when deployed. You don’t need to reload the model in memory manually. Dataflow is scalable, stream-friendly, and integrates well with Pub/Sub for input and BigQuery for output. Writing a custom DoFn in Dataflow gives you flexibility to call the endpoint using the prediction API and process results efficiently.

Exposing the model as a Vertex AI endpoint and using Dataflow with a custom DoFn provides the optimal solution for real-time predictions with minimal latency. https://cloud.google.com/blog/products/ai-machine-learning/streaming-prediction-with-dataflow-and-vertex

A is the best option because: - Minimizes Latency: Loading the model into the Cloud Function's memory eliminates the overhead of loading the model from storage for each prediction request. This significantly reduces latency, crucial for near real-time recommendations. The function is triggered directly by Pub/Sub messages, further streamlining the process. - Simplified Model Updates: Updating the model involves simply deploying a new version of the Cloud Function with the updated model. This is a much simpler process than managing pipelines or endpoints. D is the most voted so far, but... The complexity of managing the Dataflow pipeline and the potential latency introduced by the pipeline outweigh the benefits of automatic model updates using WatchFilePattern in this context. Therefore, option A (Cloud Function) remains the most efficient solution.

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.