1.11 – Testing Your Model in Azure Lesson

Lesson Overview

Testing your AI model is the key step that transforms your work from theory to practical results. In this lesson, you’ll check if your trained and deployed model in Azure accurately classifies data using real, unseen inputs. This ensures that the model’s predictions are not just academic, but genuinely useful for new data.
This lesson builds directly on the prior stages of the course where you prepared your data, trained a model (with AutoML for demonstration purposes), registered it in Azure, and deployed it to an inference endpoint. Now, you’ll take sample measurements and use Azure’s testing tool to check how the model responds.
This is a valuable process for anyone working with real datasets—whether you’re developing a proof of concept, planning to put a model into production, or just validating your workflow. For example, if you’re classifying iris flower species (as in the course), testing lets you see immediately whether your model distinguishes between similar types based on new measurement data.

The skill of preparing input data and interpreting results isn’t just about technical correctness; it’s about making sure your model is useful in any practical scenario where predictions matter.

Who This Is For

Testing your deployed Azure model is relevant if you:

• Are a data scientist or analyst ready to verify a machine learning model’s accuracy
• Need to demonstrate AI model predictions to stakeholders
• Are an educator guiding students through model deployment and validation
• Work in business or research and want reliable predictions from your models
• Have built custom or AutoML models and want to validate them in the Azure environment

Where This Fits in a Workflow

After training and deploying a model, proper testing ensures it will perform reliably with new data. In a typical project, you’ll use this lesson’s workflow after deployment and before sharing results with others or integrating the model into any product or business process.
For example, you might upload a recently trained iris classifier to Azure, deploy it as an endpoint, and then test a collection of new flower measurements to confirm the model’s accuracy. If the predictions are reliable, you can be confident with moving forward—whether for demo purposes, production use, or further tuning.

This step is essential whether you are validating a model for presentation, stress-testing with edge cases, or building automated scripts to batch-test model predictions.

Technical & Workflow Benefits

Previously, model testing often required running code locally or manually passing data into scripts, which was time-consuming and error-prone—especially for users less familiar with code. Testing directly in Azure streamlines this, allowing you to use the web interface to test your endpoint with correctly formatted data arrays.

For hands-on users, this means you can paste in one or several new data samples, control the shape and dimensions of your input, and view model outputs instantly. In practical scenarios—like evaluating multiple predictions or validating new batches of data—this method saves time and significantly reduces input errors.

Whether you’re working with AutoML-registered models or MLflow custom models, testing within Azure’s interface helps ensure consistency, reproducibility, and higher confidence in your results before moving to larger-scale integrations or sharing findings.

Practice Exercise

Take a sample from your iris dataset or similar structured measurements.

Prepare a set of four values corresponding to sepal length, sepal width, petal length, and petal width (in that order).

1. Open your model endpoint for testing in Azure.
2. Enter your single set of measurements in the required nested array format (one set per set of square brackets, with an outer wrapping set).
3. Observe the predicted class output your model returns.
   For added practice, enter two sets of measurements at once by separating them with a comma inside the outer brackets.
   

Reflection: How does the model’s prediction compare to the known class in your test data? Does the output match your expectations, or were there any surprises?

Course Context Recap

This lesson takes you through the practical testing phase, building on your training, registration, and deployment efforts in Azure Machine Learning. Previously, you learned how to prepare and deploy a model endpoint. Next, you’ll discover how to clean up your Azure resources to avoid extra costs and keep your workspace organized. Continue with the course to finish your end-to-end Azure AI model deployment journey or revisit earlier lessons for deeper practice.