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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…

This training solution is for Public Sector users of AWS Artificial Intelligence and Machine Learning services, and covers AWS Cloud concepts, AWS ML fundamentals, data science on AWS, and ML engineering on AWS.

Generative Artificial Intelligence (GenAI) uses Foundational Models (FMs) to create new content and ideas, such as conversations, stories, images, videos, and music. Generative AI has the potential to transform how organizations across the Public…

Defining problems likely to be suited for quantum computers, and programming quantum computers to solve them requires a new set of skills. To help you gain these skills, AWS recommends the following Learning Path to help Public Sector employees…

Publish an agent powered by the GitHub Copilot harness to the Copilot Studio demo website, a custom web app, Microsoft Teams, and Microsoft Copilot, and control who can use it.

Units in this learning path:

• Introduction
• Configure Authentication Publish Agent
• Deploy Demo Website Web App
• Deploy Teams Copilot
• Knowledge Check
• Summary

Products: Microsoft Copilot Studio

Roles: Developer, Maker

Level: Intermediate

Subjects: Artificial Intelligence, Chatbots

Duration: 30 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…

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…

In this course, you will learn about responsible AI practices. First, you will be introduced to what responsible AI is. You will learn how to define responsible AI, understand the challenges that responsible AI attempt to overcome and explore the…

In this learning path, you'll embark on an insightful exploration of Microsoft’s Responsible AI framework, learning how to apply ethical principles to the development and use of AI technologies. You start by learning the frameworks that shape Microsoft’s approach to responsible AI, focusing on the commitment to the six responsible AI principles. As you progress, you discover practical ways to integrate these principles into educational environments, ensuring that AI tools not only enhance learning experiences but also uphold values of fairness and privacy. The learning path concludes with hands-on exercises, translating theoretical knowledge into real-world practice. This learning path equips you with the necessary skills to implement responsible AI systems responsibly in any learning environment.

Modules in this learning path:

• Introduction to Microsofts Responsible AI Approach
• Apply Responsible AI Principles
• Embrace Responsible AI

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: 162 minutes

Learn how to use Azure Container Apps dynamic sessions to isolate AI-generated code, configure secure session pools, exchange files, and handle execution failures.

Units in this learning path:

• Introduction
• Choose Dynamic Session Pool
• Configure Secure Session Pool
• Execute Code Exchange Files
• Manage Lifecycle Failures
• Exercise Execute Code Dynamic Session
• Knowledge Check
• Summary

Products: Azure Container Apps

Roles: Developer

Level: Intermediate

Subjects: Artificial Intelligence, Backend Development

Duration: 84 minutes

Choose this module if you want to automate multistep machine learning workflows with reusable components and Azure Machine Learning pipelines.

Units in this learning path:

• Introduction
• Create Components
• Create Pipeline
• Run Pipeline Job
• Exercise Run Pipeline Job
• Knowledge Check
• Summary

Products: Azure Machine Learning

Roles: Data Scientist

Level: Beginner

Subjects: Machine Learning

Duration: 40 minutes

Choose this module if you want to convert notebooks to training scripts, run command jobs, and track models with MLflow.

Units in this learning path:

• Introduction
• Convert Notebook Script
• Run Script Command Job
• Use Parameters Command Job
• Track Metrics
• View Metrics Evaluate Models
• Exercise
• Knowledge Check
• Summary

Products: Azure Machine Learning

Roles: Data Scientist

Level: Beginner

Subjects: Machine Learning

Duration: 49 minutes