AI/ML

Architecting agentic AI business solutions is an advanced course for architects, senior consultants, and technical leaders responsible for planning, designing, and governing AI-powered enterprise solutions built on Microsoft platforms. This course serves as a foundational, real-world, and architectural preparation step that builds the design judgment, strategic reasoning, and end-to-end understanding learners need before pursuing the AB-100 exam or implementing agentic AI solutions at scale. Learners will explore how to architect AI-powered business solutions that use agents, copilots, and generative AI to automate tasks, improve decision-making, and enhance employee and customer experiences. Emphasis is placed on architecture, trade-offs, governance, cost/benefit analysis, and lifecycle management, rather than step-by-step configuration.

In this course, learners will discover how to apply generative AI to streamline daily tasks, enhance decision-making, and drive meaningful business outcomes. Learners will understand how to use Microsoft 365 Copilot and its functionalities to improve their productivity. The course focuses on real-world use cases—no coding required—making it ideal for those who want to confidently integrate AI into their work.

In this course, learners will explore how to lead AI transformation across their organization. They’ll learn practical strategies to identify high-impact AI opportunities, align investments with business goals, and champion responsible AI practices. The course emphasizes real-world applications and strategic decision-making—no technical expertise required—making it ideal for senior leaders who want to confidently drive AI adoption and innovation.

This course provides a comprehensive introduction to Microsoft 365, Copilot, and AI-powered agents. It introduces learners to the foundational concepts, core services, and administrative controls of Microsoft 365. It then builds upon this foundation by exploring how Copilot and agents can utilize AI to automate tasks, enhance collaboration, and personalize user experiences across the Microsoft 365 suite.

Natural language processing (NLP) solutions use language models to interpret the semantic meaning of written or spoken language. You can use the Language Understanding service to build language models for your applications.

This course prepares learners to design, implement, and operate Machine Learning Operations (MLOps) and Generative AI Operations (GenAIOps) solutions on Azure. It covers building secure and scalable AI infrastructure, managing the full lifecycle of traditional machine learning models with Azure Machine Learning, and deploying, evaluating, monitoring, and optimizing generative AI applications and agents using Microsoft Foundry. Learners will gain hands-on knowledge of automation, continuous integration and delivery, infrastructure as code, and observability by using tools such as GitHub Actions, Azure CLI, and Bicep. The course emphasizes collaboration with data science and DevOps teams to deliver reliable, production-ready AI systems aligned with modern MLOps and GenAIOps best practices.

This learning path explores how the Azure AI and Azure Machine Learning Services integrations provided by the Azure AI extension for Azure Database for PostgreSQL – Flexible Server can enable you to build AI-powered apps.

Do you have information locked up in structured and unstructured data sources? Using Azure AI Search, you can extract key insights from this data, and enable applications to search and analyze them.

Generative Artificial Intelligence (AI) is becoming more functional and accessible, and AI agents are a key component of this evolution. This learning path will help you understand the AI agents, including when to use them and how to build them, using Azure AI Agent Service and Semantic Kernel Agent Framework. By the end of this learning path, you will have the skills needed to develop AI agents on Azure.