1.8 – Creating A Training Job Lesson

Lesson Overview

Setting up a proper training job is a key step in building a machine learning model in Azure. This lesson focuses on the practical process of creating and submitting a job in Azure Machine Learning Studio—which is where your data, chosen algorithms, and computing resources come together to produce a trained model. The lesson begins with reviewing account costs in Azure, making sure you track usage as you proceed. It walks through navigating the Azure portal, launching the Machine Learning workspace, and preparing your dataset and compute instance for training.

You’ll learn how to use the Jobs section in Azure ML Studio, understand the difference between classification types, and configure your job to match the dataset’s properties. The module highlights the advantages of using Azure’s automated training features (AutoML), but also mentions options for more custom or advanced experimentation for further exploration. Real-world use cases for this workflow include automating predictions, building scalable data science solutions for business, or simply learning how professional ML pipelines are run in cloud environments. For anyone planning to train ML models efficiently or scale their experiments, mastering this job creation process is essential.

Who This Is For

If you’re looking to train machine learning models on Azure in a clear, controlled, and repeatable way, this lesson is a fit.

• Data scientists starting with Azure Machine Learning tools
• Developers bringing AI models into cloud-based products
• Educators teaching cloud-based data science workflows
• Analysts interested in automation and model performance tracking
• Business users who want to understand model training costs and setup

Where This Fits in a Workflow

Creating a training job is typically performed after your data is uploaded and your compute resources are set up, but before you analyze results or deploy a model. It’s the process that kicks off your actual model training in the cloud. For example, after loading the iris dataset and preparing your compute instance (such as a GPU-enabled virtual machine), this lesson guides you through configuring a job that launches and monitors your chosen algorithm as it learns from your data. You’ll use this workflow anytime you update data, try new algorithms, or want to retrain models with new configurations. It supports robust experimentation and systematic tracking for any iterative machine learning project.

Technical & Workflow Benefits

Configuring training jobs through Azure ML Studio offers notable benefits compared to manual scripting or local experimentation. Previously, you might write local code, manually manage split datasets, or monitor model performance via logs or ad-hoc scripts. With Azure, the training job setup streamlines these steps through an interface where you easily pick datasets, algorithms, and compute instances—automating data splitting and model evaluation under the hood. For tasks like classifying different types of irises, this approach saves you time, reduces setup errors, and ensures your results are tracked and reproducible. In larger projects, it brings clear cost oversight, centralizes result tracking, and enables easy scaling, all through the Azure portal.

Practice Exercise

1. With your iris dataset already uploaded and your compute instance ready in Azure ML Studio, practice setting up a training job:
   Navigate to the Jobs section in your ML workspace, ensuring your compute is started.
2. Use the “Train Automatically” feature to set up your experiment for multi-class classification, pointing to the iris dataset.
3. Configure your experiment name and review or adjust settings for evaluation metric (choose accuracy) and train/test split (set to 80/20).
   

When you finish, consider: How does setting up a job in the Azure Studio compare to running model training from a local script or notebook?

Course Context Recap

This lesson builds on your earlier work in preparing Azure resources and uploading data. After learning to set up your workspace and compute, you now use those elements to configure and start a model training job. Up next, you’ll analyze model results and explore techniques to interpret what your training job produced. Continue with the course to move from running training jobs to evaluating and deploying trained AI models in Azure. Each step brings you closer to being confident and productive with cloud-based machine learning.