AI and machine learning
Learning
Learning Paths
Build AI Apps with Azure Database for PostgreSQL.
Levels: Intermediate
Roles: Developer
Modules
- Get started with generative AI in Azure Database for PostgreSQL
- Enable semantic search in Azure Database for PostgreSQL
- Summarize data with Azure AI Services and Azure database for PostgreSQL
- Perform sentiment analysis and opinion mining with Azure Database for PostgreSQL
- Extract insights using Azure AI Language and Azure Database for PostgreSQL
- Translate Text with Azure AI Translator and Azure Database for PostgreSQL
- Use Azure Machine Learning for inferencing from Azure Database for PostgreSQL
Build an AI copilot with vCore-based Azure Cosmos DB for MongoDB and Azure OpenAI.
Levels: Intermediate
Roles: Developer
Modules
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.
Levels: Beginner
Roles: K-12 Educator, Business User, Higher Education Educator, Data Engineer, School Leader
Modules
In today's fast-paced digital era, artificial intelligence (AI) is changing the way we train and educate. This learning path is designed to equip you with an understanding of how AI tools can transform a learning environment. Throughout this learning path, you'll explore how AI can elevate the training experience, build engaging content, and foster a more inclusive training environment. You'll also explore the advantages of integrating AI-powered productivity tools into your daily educational practices. By the end of this learning path, you'll be equipped to design and implement training ecosystems that apply AI tools to enhance the training process.
Levels: Beginner
Roles: K-12 Educator, Business User, Higher Education Educator, Data Engineer, School Leader
Modules
Discover the world of generative AI, including basic concepts, the different applications in various fields, and the skills necessary to use generative AI tools within training environments. Gain knowledge of large language models (LLMs), AI-powered image generators, and Microsoft Copilot. Explore prompt engineering, navigate the limitations of content created by generative AI models, and learn to utilize generative AI models into your trainings critically and responsibly.
Levels: Beginner
Roles: K-12 Educator, Business User, Higher Education Educator, Data Engineer, School Leader
Modules
Learn how to create custom copilots with the Azure AI Studio.
Levels: Intermediate
Roles: Data Scientist, AI Engineer
Modules
APL-2005 Develop AI agents using Azure OpenAI and the Semantic Kernel SDK
Levels: Intermediate
Roles: Developer
Modules
Gain the foundational knowledge about the principles of artificial intelligence (AI) and how it's used in educational settings.
Levels: Beginner
Roles: K-12 Educator, Higher Education Educator, Business User, Data Engineer, School Leader
Modules
How to create custom computer vision solutions with Azure AI Custom Vision
Levels: Intermediate
Roles: AI Engineer, Developer
Modules
Explore the potential of artificial intelligence in education.
Levels: Beginner
Roles: K-12 Educator, School Leader, Higher Education Educator
Modules
Develop skills in securing and governing data within AI-driven environments, using Microsoft Purview to navigate AI risks and ensure compliance. This path enhances your understanding of adapting security and governance strategies in response to the evolving landscape of AI technology.
Levels: Intermediate
Roles: Administrator, Auditor, Information Protection Administrator
Modules
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.
Levels: Beginner
Roles: Data Scientist
Modules
- Make data available in Azure Machine Learning
- Work with compute targets in Azure Machine Learning
- Work with environments in Azure Machine Learning
- Run a training script as a command job in Azure Machine Learning
- Track model training with MLflow in jobs
- Register an MLflow model in Azure Machine Learning
- Deploy a model to a managed online endpoint
Learning Modules
