Topic 1 Question 143

Professional Machine Learning Engineer

Topic 1 Question 143
You have developed an ML model to detect the sentiment of users’ posts on your company's social media page to identify outages or bugs. You are using Dataflow to provide real-time predictions on data ingested from Pub/Sub. You plan to have multiple training iterations for your model and keep the latest two versions live after every run. You want to split the traffic between the versions in an 80:20 ratio, with the newest model getting the majority of the traffic. You want to keep the pipeline as simple as possible, with minimal management required. What should you do?
- Deploy the models to a Vertex AI endpoint using the traffic-split=0=80, PREVIOUS_MODEL_ID=20 configuration.
- Wrap the models inside an App Engine application using the --splits PREVIOUS_VERSION=0.2, NEW_VERSION=0.8 configuration
- Wrap the models inside a Cloud Run container using the REVISION1=20, REVISION2=80 revision configuration.
- Implement random splitting in Dataflow using beam.Partition() with a partition function calling a Vertex AI endpoint.
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- 正解だと思う選択肢: A
  A. Deploy the models to a Vertex AI endpoint using the traffic-split=0=80, PREVIOUS_MODEL_ID=20 configuration.
  
  The recommended approach to achieve the desired outcome would be to deploy the ML models to a Vertex AI endpoint and configure the traffic splitting using the traffic-split parameter. The traffic-split parameter enables you to split traffic between multiple versions of a model based on a percentage split. In this case, the newest model should receive the majority of the traffic, which can be achieved by setting the traffic-split parameter to 0=80. The previous version of the model should receive the remaining 20% of the traffic, which can be achieved by setting the PREVIOUS_MODEL_ID parameter to 20.
  
  👍 5
  TNT872023/03/07
- 正解だと思う選択肢: A
  I think is A because traffic can be split across different versions when using Endpoints https://cloud.google.com/vertex-ai/docs/general/deployment#models-endpoint. The --trafic-split flag does exist, but in the question the syntax is incorrect, it should be "--traffic-split = [MODEL_ID_1=value, MODEL_ID_2=value]" as explained in https://cloud.google.com/sdk/gcloud/reference/ai/endpoints/deploy-model
  
  👍 3
  FherRO2023/02/22
- 正解だと思う選択肢: A
  Went with A
  
  👍 1
  M252023/05/10
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