1.9 – Training Job Results Lesson

Lesson Overview

After your model finishes training in Azure Machine Learning, you’re presented with a suite of information about how it performed. This lesson walks through how to find and interpret this information. Reviewing job results helps you judge the effectiveness of your models, compare experiment runs, and gain insight into which features are making the biggest impact on predictions.

Within the Azure interface, every training run is saved as a “job” and includes a detailed record of properties and metrics. You’ll see information like run duration, overall accuracy, and test results. More advanced features, like aggregate feature importance, break down which elements of your data most influenced model decisions—useful for understanding, validating, or even improving your AI. This knowledge is especially relevant when using Azure’s automated tools, as the platform handles many of the calculations required for standard evaluation metrics.

Whether you’re building for business, academic, or personal projects, knowing how to read training job results is essential. It not only tells you if your model worked, but why and how it made its decisions, helping you get ready for the next step: making predictions with your model in production.

Who This Is For

Reviewing AI training outcomes in Azure is helpful if you:

• Are a data analyst evaluating machine learning results
• Work as a developer or engineer responsible for training models
• Need to validate models before deployment in business solutions
• Are an educator teaching machine learning concepts
• Act as a project manager overseeing AI initiatives
• Want to understand model transparency and decision drivers

Where This Fits in a Workflow

Interpreting training job results is a middle step between building and deploying your AI model. Once your training run completes, you’ll need to analyze the outcomes to confirm your model meets your goals.
For example, you might check if the model’s accuracy is high enough to use in a product, or dig into feature importance to justify your model’s decisions to stakeholders. If there are errors—such as failed jobs due to package dependencies—you’ll spot those here and address them before moving forward. This review ensures you only move on to deployment and inference with strong, reliable models.

Technical & Workflow Benefits

With Azure’s built-in tracking and metrics, you save significant time over manually coding evaluation tools. Previously, you’d have to export results, write custom scripts for performance metrics, and create your own visual charts. Azure centralizes all this: accuracy, feature importance, test splits, and even error details are presented automatically for each job.

This clarity means you can immediately see which version of your model performed “best,” what drove its decision-making, and how it fared on test data—without extra effort. For instance, you can quickly see if a model reached 80% accuracy or if a specific input feature (like a measurement) consistently influenced predictions. And if something fails, you see exactly why, so you can fix it early. The end result is a more reliable build-test-deploy cycle, with fewer surprises down the line.

Practice Exercise

Download a saved training job report from your Azure workspace (or use a sample if you don’t have your own).

1. Open the job summary and identify which experiment was marked as “best.”
2. Review the reported test accuracy and compare it to the training accuracy.
3. Examine the feature importance chart—what data field most influenced model accuracy?

Reflection: How might your next round of training change based on these results? For example, would you adjust which features to focus on or address any flagged errors in package dependencies?

Course Context Recap

This lesson builds directly on your first successful model training in Azure. Previously, you set up experiments and tracked training progress. Now, you’re learning how to interpret and evaluate the results, a crucial step before production deployment. Next, you’ll move from reviewing results to actually deploying your model and running real-time inference. Continue through the course to bring your AI model into practical use and learn how to evaluate its performance with live data.