Merge pull request #53843 from MicrosoftDocs/NEW-experiment-ml

New module - experiment ml

Author: ttorble

learn-pr/paths/build-first-machine-operations-workflow/index.yml

@@ -1,22 +1,19 @@
 ### YamlMime:LearningPath
 uid: learn.wwl.build-first-machine-operations-workflow
 metadata:
-  title: End-to-end machine learning operations (MLOps) with Azure Machine Learning
-  description: End-to-end machine learning operations (MLOps) with Azure Machine Learning
-  ms.date: 12/01/2025
+  title: Operationalize machine learning models (MLOps)
+  description: Learn the full MLOps lifecycle for machine learning models, from experimentation and pipeline automation to CI/CD, automated testing, and model deployment in production.
+  ms.date: 03/12/2026
   author: wwlpublish
   ms.author: madiepev
   ms.topic: learning-path
-  ms.custom:
-    - build-2023
-    - build-2023-dataai
-title: End-to-end machine learning operations (MLOps) with Azure Machine Learning
+title: Operationalize machine learning models (MLOps)
 prerequisites: |
   - Programming experience with Python or R
   - Experience developing and training machine learning models
   - Familiarity with basic Azure Machine Learning concepts
 summary: |
-  Machine learning operations (MLOps) applies DevOps principles to machine learning projects. In this learning path, you'll learn how to implement key concepts like source control, automation, and CI/CD to build an end-to-end MLOps solution.
+  Learn how to operationalize machine learning models using the complete MLOps lifecycle. This learning path covers experimenting and training models with Azure Machine Learning, automating model training with pipelines and hyperparameter tuning, triggering jobs with GitHub Actions, implementing trunk-based development, managing environments, and deploying models to production.
 iconUrl: /training/achievements/build-first-machine-operations-workflow.svg
 levels:
 - intermediate
@@ -29,10 +26,11 @@ subjects:
 - artificial-intelligence
 - machine-learning
 modules:
-- learn.wwl.use-azure-machine-learn-job-for-automation
+- learn.wwl.experiment-azure-machine-learning
+- learn.wwl.perform-hyperparameter-tuning-azure-machine-learning-pipelines
+- learn.wwl.run-pipelines-azure-machine-learning
 - learn.wwl.trigger-azure-machine-learn-jobs-github-actions
 - learn.wwl.trigger-github-actions-trunk-based-development
-- learn.wwl.work-linting-unit-test-github-actions
 - learn.wwl.work-environments-github-actions
 - learn.wwl.deploy-model-github-actions
 trophy:

learn-pr/wwl-data-ai/experiment-azure-machine-learning/1-introduction.yml

@@ -0,0 +1,16 @@
+### YamlMime:ModuleUnit
+uid: learn.wwl.experiment-azure-machine-learning.introduction
+title: Introduction
+metadata:
+  title: Introduction
+  description: Learn how to find the best model with automated machine learning (AutoML) and by experimenting in notebooks in Azure Machine Learning.
+  author: wwlpublish
+  ms.author: madiepev
+  ms.date: 03/11/2026
+  ms.topic: unit
+  ms.collection:
+    - wwl-ai-copilot
+  ms.custom: devx-track-python
+durationInMinutes: 2
+content: |
+  [!include[](includes/1-introduction.md)]

learn-pr/wwl-data-ai/experiment-azure-machine-learning/10-summary.yml

@@ -0,0 +1,15 @@
+### YamlMime:ModuleUnit
+uid: learn.wwl.experiment-azure-machine-learning.summary
+title: Summary
+metadata:
+  title: Summary
+  description: Summary of how to find the best model with Azure Machine Learning, using AutoML and MLflow-tracked notebooks.
+  author: wwlpublish
+  ms.author: madiepev
+  ms.date: 03/11/2026
+  ms.topic: unit
+  ms.collection:
+    - wwl-ai-copilot
+durationInMinutes: 1
+content: |
+  [!include[](includes/9-summary.md)]

learn-pr/wwl-data-ai/experiment-azure-machine-learning/2-preprocess-data-configure-featurization.yml

@@ -0,0 +1,16 @@
+### YamlMime:ModuleUnit
+uid: learn.wwl.experiment-azure-machine-learning.preprocess-data-configure-featurization
+title: Preprocess data and configure featurization
+metadata:
+  title: Preprocess data and configure featurization
+  description: Learn how to configure automated machine learning to preprocess data and perform featurization with the Python SDK (v2) for Azure Machine Learning.
+  author: wwlpublish
+  ms.author: madiepev
+  ms.date: 03/11/2026
+  ms.topic: unit
+  ms.collection:
+    - wwl-ai-copilot
+  ms.custom: devx-track-python
+durationInMinutes: 3
+content: |
+  [!include[](includes/2-preprocess-data-configure-featurization.md)]

learn-pr/wwl-data-ai/experiment-azure-machine-learning/3-run-job.yml

@@ -0,0 +1,16 @@
+### YamlMime:ModuleUnit
+uid: learn.wwl.experiment-azure-machine-learning.run-job
+title: Run an automated machine learning experiment
+metadata:
+  title: Run an Automated Machine Learning experiment
+  description: Learn how to configure and submit an automated machine learning experiment run with the Python SDK (v2) for Azure Machine Learning.
+  author: wwlpublish
+  ms.author: madiepev
+  ms.date: 03/11/2026
+  ms.topic: unit
+  ms.collection:
+    - wwl-ai-copilot
+  ms.custom: devx-track-python
+durationInMinutes: 5
+content: |
+  [!include[](includes/3-run-job.md)]

learn-pr/wwl-data-ai/experiment-azure-machine-learning/4-evaluate-compare-models.yml

@@ -0,0 +1,16 @@
+### YamlMime:ModuleUnit
+uid: learn.wwl.experiment-azure-machine-learning.evaluate-compare-models
+title: Evaluate and compare models
+metadata:
+  title: Evaluate and compare models
+  description: Learn how to evaluate and compare models trained by automated machine learning in Azure Machine Learning.
+  author: wwlpublish
+  ms.author: madiepev
+  ms.date: 03/11/2026
+  ms.topic: unit
+  ms.collection:
+    - wwl-ai-copilot
+  ms.custom: devx-track-python
+durationInMinutes: 5
+content: |
+  [!include[](includes/4-evaluate-compare-models.md)]

learn-pr/wwl-data-ai/experiment-azure-machine-learning/5-model-tracking.yml

@@ -0,0 +1,16 @@
+### YamlMime:ModuleUnit
+uid: learn.wwl.experiment-azure-machine-learning.model-tracking
+title: Configure MLflow for model tracking in notebooks
+metadata:
+  title: Configure MLflow for model tracking in notebooks
+  description: Learn how to configure MLflow for model tracking when experimenting in notebooks in Azure Machine Learning.
+  author: wwlpublish
+  ms.author: madiepev
+  ms.date: 03/11/2026
+  ms.topic: unit
+  ms.collection:
+    - wwl-ai-copilot
+  ms.custom: devx-track-python
+durationInMinutes: 4
+content: |
+  [!include[](includes/5-model-tracking.md)]

learn-pr/wwl-data-ai/experiment-azure-machine-learning/6-train-models-notebooks.yml

@@ -0,0 +1,16 @@
+### YamlMime:ModuleUnit
+uid: learn.wwl.experiment-azure-machine-learning.train-models-notebooks
+title: Train and track models in notebooks
+metadata:
+  title: Train and track models in notebooks
+  description: Learn how to train and track models in Jupyter notebooks using MLflow in Azure Machine Learning.
+  author: wwlpublish
+  ms.author: madiepev
+  ms.date: 03/11/2026
+  ms.topic: unit
+  ms.collection:
+    - wwl-ai-copilot
+  ms.custom: devx-track-python
+durationInMinutes: 5
+content: |
+  [!include[](includes/6-train-models-notebooks.md)]

learn-pr/wwl-data-ai/experiment-azure-machine-learning/7-responsible-ai-dashboard.yml

@@ -0,0 +1,15 @@
+### YamlMime:ModuleUnit
+uid: learn.wwl.experiment-azure-machine-learning.responsible-ai-dashboard
+title: Evaluate models with the Responsible AI dashboard
+metadata:
+  title: Evaluate models with the Responsible AI dashboard
+  description: Learn how to evaluate a trained model using the Responsible AI dashboard in Azure Machine Learning.
+  author: wwlpublish
+  ms.author: madiepev
+  ms.date: 03/12/2026
+  ms.topic: unit
+  ms.collection:
+    - wwl-ai-copilot
+durationInMinutes: 5
+content: |
+  [!include[](includes/7-responsible-ai-dashboard.md)]

learn-pr/wwl-data-ai/experiment-azure-machine-learning/8-exercise.yml

@@ -0,0 +1,16 @@
+### YamlMime:ModuleUnit
+uid: learn.wwl.experiment-azure-machine-learning.exercise
+title: Exercise - Find the best classification model with Azure Machine Learning
+metadata:
+  title: Exercise - Find the best classification model with Azure Machine Learning
+  description: Practice finding the best classification model by using AutoML and MLflow-tracked notebooks in Azure Machine Learning.
+  author: wwlpublish
+  ms.author: madiepev
+  ms.date: 03/11/2026
+  ms.topic: unit
+  ms.collection:
+    - wwl-ai-copilot
+  ms.custom: devx-track-python
+durationInMinutes: 30
+content: |
+  [!include[](includes/8-exercise.md)]