Discover proven strategies for testing new versions of machine learning models in Azure. Learn how online inferencing and traffic splitting can optimize your model deployment process while ensuring robust performance.
Table of Contents
Question
When using Azure Machine Learning, how can you test new versions of a machine learning model?
A. Create a local minimum for the model.
B. Use online inferencing and traffic splitting.
C. Observe the learning rate for the model.
D. Deploy the model to production manually via CLI.
Answer
B. Use online inferencing and traffic splitting.
Explanation
The best way to test new versions of a machine learning model in Azure Machine Learning is by using online inferencing and traffic splitting. These allow you to deploy the new model version alongside the existing one, and send a small percentage of traffic (e.g., 10%) to the new version for evaluation. This enables you to compare the performance of both versions on real-world data in a controlled manner. This process works as follows:
- Online Inferencing: This refers to deploying your machine learning model as a web service that can receive real-time requests and generate predictions. In Azure Machine Learning, you can create online endpoints to achieve this.
- Traffic Splitting: This technique allows you to control how incoming traffic (requests for predictions) is routed between your deployed models. With traffic splitting, you can direct a specific percentage of traffic (e.g., 10%) to the new model version’s endpoint, while the remaining 90% continues to be routed to the existing model.
Observing the learning rate does not directly test the performance of a new model version. It is a metric used during training to determine how quickly the model learns, not for testing new versions.
Creating a local minimum for the model is not a testing strategy. This refers to suboptimal performance due to insufficient training or bad initialization.
Deploying the model to production manually via a command line interface (CLI) is not recommended. It is risky and bypasses the advantages of controlled testing. It could lead to unexpected issues if the new version performs poorly.
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