v1 train models

Author: s-polly

articles/machine-learning/v1/concept-train-machine-learning-model.md

@@ -9,7 +9,7 @@ ms.author: manashg
 ms.reviewer: ssalgado
 ms.subservice: core
 ms.topic: concept-article
-ms.date: 08/30/2022
+ms.date: 09/10/2025
 ms.custom: UpdateFrequency5, devx-track-python, devx-track-azurecli
 ms.devlang: azurecli
 ---
@@ -20,35 +20,36 @@ ms.devlang: azurecli
 
 [!INCLUDE [v1 deprecation](../includes/sdk-v1-deprecation.md)]
 
-Azure Machine Learning provides several ways to train your models, from code-first solutions using the SDK to low-code solutions such as automated machine learning and the visual designer. Use the following list to determine which training method is right for you:
+Azure Machine Learning offers multiple ways to train models, including code-first solutions with the SDK and low-code options like automated machine learning and the visual designer. Use the following list to determine which training method fits your needs:
 
 + [Azure Machine Learning SDK for Python](#python-sdk): The Python SDK provides several ways to train models, each with different capabilities.
 
     | Training method | Description |
     | ----- | ----- |
-    | [Run configuration](#run-configuration) | A **typical way to train models** is to use a training script and job configuration. The job configuration provides the information needed to configure the training environment used to train your model. You can specify your training script, compute target, and Azure Machine Learning environment in your job configuration and run a training job. |
-    | [Automated machine learning](#automated-machine-learning) | Automated machine learning allows you to **train models without extensive data science or programming knowledge**. For people with a data science and programming background, it provides a way to save time and resources by automating algorithm selection and hyperparameter tuning. You don't have to worry about defining a job configuration when using automated machine learning. |
-    | [Machine learning pipeline](#machine-learning-pipeline) | Pipelines are not a different training method, but a **way of defining a workflow using modular, reusable steps** that can include training as part of the workflow. Machine learning pipelines support using automated machine learning and run configuration to train models. Since pipelines are not focused specifically on training, the reasons for using a pipeline are more varied than the other training methods. Generally, you might use a pipeline when:<br>* You want to **schedule unattended processes** such as long running training jobs or data preparation.<br>* Use **multiple steps** that are coordinated across heterogeneous compute resources and storage locations.<br>* Use the pipeline as a **reusable template** for specific scenarios, such as retraining or batch scoring.<br>* **Track and version data sources, inputs, and outputs** for your workflow.<br>* Your workflow is **implemented by different teams that work on specific steps independently**. Steps can then be joined together in a pipeline to implement the workflow. |
+    | [Run configuration](#run-configuration) | A **common way to train models** is to use a training script and job configuration. The job configuration defines the training environment, including your script, compute target, and Azure Machine Learning environment. You can run a training job by specifying these details. |
+    | [Automated machine learning](#automated-machine-learning) | Automated machine learning lets you **train models without deep data science or programming expertise**. For experienced users, it saves time by automating algorithm selection and hyperparameter tuning. Job configuration is not required when using automated machine learning. |
+    | [Machine learning pipeline](#machine-learning-pipeline) | Pipelines are not a separate training method, but a **way to define workflows using modular, reusable steps** that can include training. Pipelines support both automated machine learning and run configuration. Use a pipeline when you want to:<br>* **Schedule unattended processes** like long-running training jobs or data preparation.<br>* Coordinate **multiple steps** across different compute resources and storage locations.<br>* Create a **reusable template** for scenarios such as retraining or batch scoring.<br>* **Track and version data sources, inputs, and outputs** for your workflow.<br>* Enable **different teams to work on specific steps independently** and combine them in a pipeline. |
 
-+ **Designer**: Azure Machine Learning designer provides an easy entry-point into machine learning for building proof of concepts, or for users with little coding experience. It allows you to train models using a drag and drop web-based UI. You can use Python code as part of the design, or train models without writing any code.
++ **Designer**: Azure Machine Learning designer is an easy entry point for building proof of concepts or for users with limited coding experience. Train models using a drag-and-drop web UI. You can include Python code or train models without writing any code.
 
-+ **Azure CLI**: The machine learning CLI provides commands for common tasks with Azure Machine Learning, and is often used for **scripting and automating tasks**. For example, once you've created a training script or pipeline, you might use the Azure CLI to start a training job on a schedule or when the data files used for training are updated. For training models, it provides commands that submit training jobs. It can submit jobs using run configurations or pipelines.
++ **Azure CLI**: The machine learning CLI offers commands for common Azure Machine Learning tasks and is often used for **scripting and automation**. For example, after creating a training script or pipeline, you can use the CLI to start a training job on a schedule or when training data is updated. The CLI can submit jobs using run configurations or pipelines.
 
-Each of these training methods can use different types of compute resources for training. Collectively, these resources are referred to as [__compute targets__](concept-azure-machine-learning-architecture.md#compute-targets). A compute target can be a local machine or a cloud resource, such as an Azure Machine Learning Compute, Azure HDInsight, or a remote virtual machine.
+Each training method can use different types of compute resources, called [__compute targets__](concept-azure-machine-learning-architecture.md#compute-targets). A compute target can be a local machine or a cloud resource, such as Azure Machine Learning Compute, Azure HDInsight, or a remote virtual machine.
 
 ## Python SDK
 
-The Azure Machine Learning SDK for Python allows you to build and run machine learning workflows with Azure Machine Learning. You can interact with the service from an interactive Python session, Jupyter Notebooks, Visual Studio Code, or other IDE.
+The Azure Machine Learning SDK for Python lets you build and run machine learning workflows. You can interact with the service from an interactive Python session, Jupyter Notebooks, Visual Studio Code, or other IDE.
 
 * [What is the Azure Machine Learning SDK for Python](/python/api/overview/azure/ml/intro)
 * [Install/update the SDK](/python/api/overview/azure/ml/install)
 * [Configure a development environment for Azure Machine Learning](how-to-configure-environment.md)
 
 ### Run configuration
 
-A generic training job with Azure Machine Learning can be defined using the [ScriptRunConfig](/python/api/azureml-core/azureml.core.scriptrunconfig). The script run configuration is then used, along with your training script(s) to train a model on a compute target.
 
-You may start with a run configuration for your local computer, and then switch to one for a cloud-based compute target as needed. When changing the compute target, you only change the run configuration you use. A run also logs information about the training job, such as the inputs, outputs, and logs.
+A typical training job in Azure Machine Learning is defined using [ScriptRunConfig](/python/api/azureml-core/azureml.core.scriptrunconfig). The script run configuration, together with your training script(s), is used to train a model on a compute target.
+
+You can start with a run configuration for your local computer and switch to a cloud-based compute target as needed. To change the compute target, update the run configuration. Each run logs information about the training job, including inputs, outputs, and logs.
 
 * [What is a run configuration?](concept-azure-machine-learning-architecture.md#run-configurations)
 * [Tutorial: Train your first ML model](tutorial-1st-experiment-sdk-train.md)
@@ -57,10 +58,11 @@ You may start with a run configuration for your local computer, and then switch
 
 ### Automated Machine Learning
 
-Define the iterations, hyperparameter settings, featurization, and other settings. During training, Azure Machine Learning tries different algorithms and parameters in parallel. Training stops once it hits the exit criteria you defined.
+Define iterations, hyperparameter settings, featurization, and other options. During training, Azure Machine Learning tests different algorithms and parameters in parallel. Training stops when it meets the exit criteria you set.
+
 
 > [!TIP]
-> In addition to the Python SDK, you can also use Automated ML through [Azure Machine Learning studio](https://ml.azure.com).
+> You can also use Automated ML through [Azure Machine Learning studio](https://ml.azure.com), in addition to the Python SDK.
 
 * [What is automated machine learning?](../concept-automated-ml.md)
 * [Tutorial: Create your first classification model with automated machine learning](../tutorial-first-experiment-automated-ml.md)
@@ -71,7 +73,8 @@ Define the iterations, hyperparameter settings, featurization, and other setting
 
 ### Machine learning pipeline
 
-Machine learning pipelines can use the previously mentioned training methods. Pipelines are more about creating a workflow, so they encompass more than just the training of models. In a pipeline, you can train a model using automated machine learning or run configurations.
+
+Machine learning pipelines can use the training methods described above. Pipelines focus on creating workflows, so they cover more than just model training. In a pipeline, you can train a model using automated machine learning or run configurations.
 
 * [What are ML pipelines in Azure Machine Learning?](../concept-ml-pipelines.md)
 * [Create and run machine learning pipelines with Azure Machine Learning SDK](how-to-create-machine-learning-pipelines.md)
@@ -81,40 +84,43 @@ Machine learning pipelines can use the previously mentioned training methods. Pi
 
 ### Understand what happens when you submit a training job
 
-The Azure training lifecycle consists of:
+
+The Azure training lifecycle includes:
 
 1. Zipping the files in your project folder, ignoring those specified in _.amlignore_ or _.gitignore_
-1. Scaling up your compute cluster 
-1. Building or downloading the dockerfile to the compute node 
-    1. The system calculates a hash of: 
-        - The base image 
+2. Scaling up your compute cluster
+3. Building or downloading the Docker image to the compute node
+    1. The system calculates a hash of:
+        - The base image
         - The conda definition YAML (see [Create & use software environments in Azure Machine Learning](how-to-use-environments.md))
-    1. The system uses this hash as the key in a lookup of the workspace Azure Container Registry (ACR)
-    1. If it is not found, it looks for a match in the global ACR
-    1. If it is not found, the system builds a new image (which will be cached and registered with the workspace ACR)
-1. Downloading your zipped project file to temporary storage on the compute node
-1. Unzipping the project file
-1. The compute node executing `python <entry script> <arguments>`
-1. Saving logs, model files, and other files written to `./outputs` to the storage account associated with the workspace
-1. Scaling down compute, including removing temporary storage 
+    2. The system uses this hash to look up the workspace Azure Container Registry (ACR)
+    3. If not found, it checks the global ACR
+    4. If still not found, the system builds a new image (which is cached and registered with the workspace ACR)
+4. Downloading your zipped project file to temporary storage on the compute node
+5. Unzipping the project file
+6. The compute node executes `python <entry script> <arguments>`
+7. Saving logs, model files, and other files written to `./outputs` to the storage account associated with the workspace
+8. Scaling down compute, including removing temporary storage
 
-If you choose to train on your local machine ("configure as local run"), you do not need to use Docker. You may use Docker locally if you choose (see the section [Configure ML pipeline](how-to-debug-pipelines.md) for an example).
+If you train on your local machine ("configure as local run"), Docker is not required. You can use Docker locally if you prefer (see [Configure ML pipeline](how-to-debug-pipelines.md) for an example).
 
 ## Azure Machine Learning designer
 
-The designer lets you train models using a drag and drop interface in your web browser.
+
+The designer lets you train models using a drag-and-drop interface in your web browser.
 
 + [What is the designer?](concept-designer.md)
 + [Tutorial: Predict automobile price](tutorial-designer-automobile-price-train-score.md)
 
 ## Azure CLI
 
-The machine learning CLI is an extension for the Azure CLI. It provides cross-platform CLI commands for working with Azure Machine Learning. Typically, you use the CLI to automate tasks, such as training a machine learning model.
+
+The machine learning CLI is an extension for the Azure CLI. It provides cross-platform commands for working with Azure Machine Learning. Typically, you use the CLI to automate tasks, such as training a machine learning model.
 
 * [Use the CLI extension for Azure Machine Learning](reference-azure-machine-learning-cli.md)
 * [MLOps on Azure](https://github.com/microsoft/MLOps)
 
 
 ## Next steps
 
-Learn how to [Configure a training run](how-to-set-up-training-targets.md).
+Learn how to [configure a training run](how-to-set-up-training-targets.md).