Explore digital courses

Learn the full GenAIOps lifecycle for generative AI applications, from planning and prompt management to evaluation, automated testing, monitoring, and tracing in production.

Modules in this learning path:

• Plan Prepare Genaiops
• Prompt Versioning Genaiops
• Evaluate Optimize Agents
• Automated Evaluation Genaiops
• Monitor Generative AI App
• Tracing Generative AI App

Products: Foundry Tools, Microsoft Foundry, Github

Roles: Data Scientist, AI Engineer, Devops Engineer

Level: Intermediate

Subjects: Artificial Intelligence, Machine Learning, Natural Language Processing, Devops

Duration: 368 minutes

Learn the full MLOps lifecycle for machine learning models, from experimentation and pipeline automation to CI/CD, automated testing, and model deployment in production.

Modules in this learning path:

• Experiment Azure Machine Learning
• Perform Hyperparameter Tuning Azure Machine Learning Pipelines
• Run Pipelines Azure Machine Learning
• Trigger Azure Machine Learn Jobs Github Actions
• Trigger Github Actions Trunk Based Development
• Work Environments Github Actions
• Deploy Model Github Actions

Products: Azure Machine Learning, Github

Roles: Data Scientist

Level: Intermediate

Subjects: Artificial Intelligence, Machine Learning

Duration: 291 minutes

Operationalize your Azure Data Factory or Azure Synapse Pipeline

Units in this learning path:

• Introduction
• Understand Language Support Azure Data Factory
• Manage Source Control of Solutions
• Continuous Integration and Deployment
• Monitor
• Set Up Alerts Azure Data Factory
• Re Run Azure Data Factory Pipelines
• Knowledge Check
• Summary

Products: Azure Data Factory, Azure Synapse Analytics

Roles: Data Engineer, Data Scientist

Level: Intermediate

Duration: 47 minutes

This course was developed by members of AWS Technical Field Communities (TFC), an AWS community of technical experts. The content is intended to complement our standard training curriculum and augment your AWS learning journey. We are aware some…

Learn how to optimize deployment and infrastructure costs using Azure Virtual Desktops native scaling tool Autoscale. Learn how to configure Autoscale so that session host VMs are only running when you need them to be.

Units in this learning path:

• Introduction
• Configure Automation
• Create Scale
• Knowledge Check
• Summary and Knowledge Check

Products: M365, Azure, Azure Virtual Desktop

Roles: Administrator

Level: Intermediate

Subjects: Cloud Computing, Finance Accounting, Remote Hybrid Work

Duration: 14 minutes

Use Power BI to understand where your money is being spent in Azure so you can help your organization to optimize its costs.

Units in this learning path:

• Introduction
• Analyze Costs Power Bi
• Hybrid Benefits Power Bi
• Reservations Power Bi
• Data Connector Power Bi
• Knowledge Check
• Summary

Products: Azure, Azure Cost Management

Roles: Administrator, Developer

Level: Beginner

Subjects: Finance Accounting, Data Analytics

Duration: 27 minutes

Learn how to apply SRE (Site Reliability Engineering) principles and use Azure SRE Agent to improve incident response, reduce operational toil, and govern remediation workflows.

Units in this learning path:

• Introduction
• Sre Azure Context
• What Why Sre Agent
• Architecture
• Setup Deployment
• Use Cases
• Governance
• Agent
• Summary

Products: Azure, Azure App Service, Azure Monitor

Roles: Administrator, Devops Engineer, Support Engineer

Level: Intermediate

Subjects: Cloud Computing, Devops

Duration: 93 minutes

Learn the concepts of agentic DevOps. Next, learn how to use GitHub Copilot, Azure DevOps MCP Server, and Azure SRE Agent to accelerate delivery, automate operations, and improve reliability across your DevOps workflows.

Modules in this learning path:

• Introduction Agentic Devops Microsoft Tools Azure
• Manage Azure Boards Using Github Copilot
• Manage Ado Mcp Server
• Optimize Azure Reliability Using Sre Agent

Products: Azure, Azure Devops, Azure Boards, Github

Roles: Administrator, Developer, Devops Engineer

Level: Intermediate

Subjects: Devops

Duration: 368 minutes

Learn about Microsoft Security Copilot and Intune for securing corporate IT. Enhance security operations, get contextual responses, and improve incident response. Implement naming conventions and manage devices efficiently.

Units in this learning path:

• Introduction
• Understand Benefits Microsoft Security Copilot Intune
• Implement Strong Naming Conventions
• Rename Device Microsoft Intune
• Add Groups Organize Users Devices
• Understand Authentication Microsoft Copilot Security
• Integrate Microsoft Security Copilot Microsoft Intune
• Leverage Prompting Features Microsoft Copilot Security
• Sample Prompts Microsoft Intune
• Knowledge Check
• Summary

Products: Intune, Ms Copilot

Roles: Administrator, Security Engineer, Security Operations Analyst, Solution Architect, Support Engineer

Level: Beginner

Subjects: Data Analytics, It Management Monitoring, Threat Protection

Duration: 47 minutes

Performance optimization, also known as performance tuning, involves making changes to the current state of the semantic model so that it runs more efficiently. Essentially, when your semantic model is optimized, it performs better.

Units in this learning path:

• 1 Introduction
• 1a Optimization Techniques
• 2 Performance
• 3 Variables
• 4 Reduce Cardinality
• 5 Directquery Models
• 6 Aggregations
• 7 Check
• 8 Summary

Products: Power Bi, Power Platform

Roles: Data Analyst

Level: Intermediate

Subjects: Data Modeling

Duration: 69 minutes

This module explores comprehensive best practices for Microsoft 365 Copilot and key extension methods, ensuring you harness the full potential and versatility of Copilot's functionality.

Units in this learning path:

• Introduction
• Examine Working with AI
• Best Practices Copilot
• Examine Effective Prompt
• Review Prompting Best Practices
• Extend Copilot with Plugins
• Knowledge Check
• Summary

Products: M365, Office 365, Ms Copilot, M365 Apps

Roles: Administrator, Business Owner, Business User

Level: Beginner

Duration: 30 minutes

Learn how to select fine-tuning methods (SFT, RFT, DPO), recognize agent quality problems, prepare training data, and design optimization strategies using hyperparameter configuration and iterative evaluation.

Units in this learning path:

• Introduction
• Select Finetune Method
• Scenarios
• Prepare Data
• Optimize Finetune
• Exercise
• Knowledge Check
• Summary

Products: Microsoft Foundry

Roles: Data Scientist, AI Engineer

Level: Intermediate

Subjects: Artificial Intelligence, Machine Learning, Natural Language Processing

Duration: 89 minutes