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Learn how to track model training with MLflow in jobs when running scripts.

Units in this learning path:

• Introduction
• Track Metrics Mlflow
• View Metrics Evaluate Models
• Exercise Use Mlflow Track Training Jobs
• Knowledge Check
• Summary

Products: Azure Machine Learning

Roles: Data Scientist

Level: Beginner

Subjects: Machine Learning

Duration: 34 minutes

In this course, you will learn the benefits and technical concepts of trails for AWS CloudTrail. Using trails for CloudTrail, you can archive, analyze, and respond to changes in your Amazon Web Services (AWS) resources. A trail is a configuration…

Learn how to train machine learning models using Spark and the MLlib library in Azure Databricks.

Units in this learning path:

• Introduction
• Understand Machine Learning
• Machine Learning Azure Databricks
• Prepare Data for Machine Learning
• Train Model
• Evaluate Model
• Exercise Machine Learning Databricks
• Knowledge Check
• Summary

Products: Azure Databricks

Roles: Data Scientist

Level: Intermediate

Subjects: Data Management, Machine Learning

Duration: 84 minutes

Train and evaluate classification models

Units in this learning path:

• Introduction
• What Is Classification
• Exercise Model
• Evaluate Classification Models
• Exercise Alternative Classification Metrics
• Multiclass Classification
• Exercise Multiclass Classification
• Knowledge Check
• Summary

Products: Azure

Roles: Data Scientist

Level: Intermediate

Subjects: Machine Learning, Classification Analysis

Duration: 64 minutes

Clustering is a type of machine learning that is used to group similar items into clusters.

Units in this learning path:

• Introduction
• What Is Clustering
• Exercise Model
• Different Types Clustering
• Exercise New Models
• Knowledge Check
• Summary

Products: Azure

Roles: Data Scientist

Level: Intermediate

Subjects: Machine Learning

Duration: 46 minutes

Train and evaluate deep learning models

Units in this learning path:

• Introduction
• Deep Neural Network Concepts
• Convolutional Neural Networks
• Transfer Learning
• Exercise Deep Learning
• Knowledge Check
• Summary

Products: Azure

Roles: Data Scientist

Level: Advanced

Subjects: Machine Learning

Duration: 47 minutes

Regression is a commonly used kind of machine learning for predicting numeric values.

Units in this learning path:

• Introduction
• What Is Regression
• Exercise Model
• Discover New Regression Models
• Exercise Powerful Models
• Improve Models
• Exercise Optimize Save Models
• Knowledge Check
• Summary

Products: Azure

Roles: Data Scientist

Level: Intermediate

Subjects: Machine Learning

Duration: 69 minutes

To train a machine learning model with Azure Machine Learning, you need to make data available and configure compute. After training your model and tracking model metrics with MLflow, you can decide to deploy your model to an endpoint for real-time predictions. (DP-3007)

Modules in this learning path:

• Make Data Available Azure Machine Learning
• Work Compute Resources Azure Machine Learning
• Work Environments Azure Machine Learning
• Run Training Script Command Job Azure Machine Learning
• Train Models Training Mlflow Jobs
• Register Mlflow Model Azure Machine Learning
• Deploy Model Managed Online Endpoint

Products: Azure Machine Learning

Roles: Data Scientist

Level: Beginner

Subjects: Machine Learning

Duration: 274 minutes

Learn how to train machine learning models in notebooks and track your work with MLflow experiments in Microsoft Fabric.

Units in this learning path:

• Introduction
• Train Model
• Mlflow
• Fabric Models
• Exercise
• Knowledge Check
• Summary

Products: Fabric

Roles: Data Analyst, Data Engineer, Data Scientist

Level: Beginner

Subjects: Data Management, Machine Learning

Duration: 54 minutes

Learn how to use deep learning libraries like PyTorch in Azure Databricks, and to distribute training by using TorchDistributor

Units in this learning path:

• Introduction
• Deep Learning
• Pytorch
• Horovod
• Exercise Deep Learning
• Knowledge Check
• Summary

Products: Azure Databricks

Roles: Data Scientist

Level: Advanced

Subjects: Data Management, Machine Learning

Duration: 70 minutes

In this module, you learn various AI-powered tools and how to effectively use them in your training sessions for an optimized learning experience.

Units in this learning path:

• Introduction
• Explore Microsoft AI Powered Tools for Trainers
• Build Training Plans with Microsoft Copilot
• Poster Design with Microsoft Designer
• Knowledge Check
• Summary

Products: Foundry Tools, Azure Machine Learning, Azure Machine Learning Designer, M365 Apps, Azure Translator Text, Azure Translator Speech, Ms Copilot, Language Service

Roles: K 12 Educator, Business User, Higher Ed Educator, Data Engineer, School Leader

Level: Beginner

Subjects: Artificial Intelligence

Duration: 116 minutes

The VEPS series covers the MSLE program providing an understanding of all aspects of the program from joining MSLE, preparing to teach a course, closing out a course, and participating in the MSLE Teams-based community.

Units in this learning path:

• Introduction
• Register Your Course Microsoft Learn Educators Portal
• Learn How Set Up Your Lab Seats Access Azure Credits
• Execute Learn Management System Integration
• Access Technical Training
• Determine How You and Your Students Will Get Certified
• Finalize Course Preparation
• Knowledge Check
• Summary

Products: Azure

Roles: Higher Ed Educator

Level: Advanced

Duration: 47 minutes