Azure ML Traffic Splitting for Model Validation

AI-300 By islever Editorial Last updated

Deploy machine learning models for production environments

Short answer

A Configure traffic splitting between deployments to route a portion of inference requests to the new model version for safe validation.

Question

A team is deploying machine learning models to a production inference endpoint in Azure Machine Learning.
The team requires a safe way to validate a new model version without disrupting existing users.
You need to recommend a deployment strategy for controlled testing of a new model version.
What should you configure?
  1. traffic splitting between deployments Correct Answer
  2. the model asset version in the registry
  3. deployment to a separate staging endpoint
  4. an evaluation script in Azure Machine Learning

Key Takeaways

The question tests the ability to distinguish between offline evaluation and live traffic management strategies, specifically identifying traffic splitting as the method for non-disruptive canary testing.

Learn how to use traffic splitting in Azure Machine Learning to safely validate new model versions without disrupting production users.

Common mistake: Candidates may choose option C (staging endpoint) because it isolates testing, but it fails to address the requirement for controlled testing *within* the production inference endpoint using live traffic distribution.

Expert Analysis

The core requirement is to validate a new model version without disrupting existing users on an inference endpoint. This scenario describes a classic canary deployment or A/B testing pattern where traffic is routed between different versions of the same resource.

Traffic Splitting (Option A): Azure Machine Learning supports deploying multiple model versions to the same scoring URI and configuring traffic weights (e.g., 90% to production, 10% to the new version). This allows the team to monitor metrics, latency, and accuracy of the new model against real-world traffic while keeping the majority of users on the stable version. If issues arise, traffic can be shifted back immediately. This directly satisfies the "safe way" and "without disrupting existing users" constraints.

Staging Endpoint (Option C): While deploying to a separate staging environment isolates the test, it does not provide controlled testing on the production endpoint with live user traffic distribution. It is often used for pre-production validation but lacks the granular traffic control of splitting within the same endpoint configuration.

Model Asset Version (Option B): This is a registry concept, not a deployment strategy. Changing the registry version does not dictate how inference requests are routed or tested.

Evaluation Script (Option D): Evaluation scripts are used for offline or online validation metrics but do not inherently manage the routing of live traffic to new versions. They support the decision-making process but are not the deployment mechanism itself.

Therefore, traffic splitting is the correct architectural pattern for controlled, non-disruptive validation in this context.

Official Reference

https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-traffic-split

This link opens the official vendor documentation in a new tab.

Exam Strategy

When a question mentions 'production inference,' 'new version,' and 'without disrupting users,' prioritize options that allow gradual rollout or traffic management (like traffic splitting or blue-green deployments). Differentiate these from isolated testing environments (staging) which do not involve live traffic mixing, and from evaluation tools which measure quality but do not manage routing.

Frequently Asked Questions

What is traffic splitting in Azure Machine Learning?

Traffic splitting allows you to deploy multiple model versions to the same endpoint and assign weight percentages to each. This enables canary testing by directing a small percentage of live traffic to the new version.

Why not use a staging endpoint for validation?

A staging endpoint provides isolation but does not test the model against the actual production load or user behavior in a mixed environment. Traffic splitting allows for controlled validation within the production infrastructure.

More AI-300 FAQ →

Community Votes

A
100%

100% of anonymous learners picked answer A. Votes are pick records left by other test-takers — they are not the verified answer.

Discussion 2 contributions

Compiled from public community threads and reader submissions, lightly edited for clarity. Handles and vote tallies are shown as recorded, and comments reflect the views of individual contributors.

From public community threads 2

  1. RICHARDALEX007 👍 1 Answered A
    Safely validating a new model version without disrupting users on an inference endpoint is traffic splitting across deployments (canary). A staging endpoint (C) doesn't test against real production traffic share; registry version (B) and an eval script (D) don't control rollout.
  2. exam_upskill 👍 1 Answered A
    A. traffic splitting between deployments

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