AI-100: Designing and Implementing an Azure AI Solutions

Clear and Concise

Course Update:

While the original content is based on the AI-100 exam, learners preparing for AI-102 can still benefit from the existing modules, as the core concepts and practical knowledge remain highly relevant and applicable to the updated certification.

Course Overview:
Microsoft Azure provides a comprehensive suite of services designed to enable rapid development, deployment, and operationalization of intelligent AI-driven solutions. This course is structured to help you understand how these services integrate to support the design, implementation, monitoring, optimization, and security of AI applications in real-world scenarios.

Originally tailored for the Microsoft AI-100 certification exam, the course remains highly valuable for those pursuing AI-102, as it covers the foundational and advanced topics that are critical to success in the evolving AI landscape on Azure.

What You’ll Learn:
The course offers deep, hands-on exploration of Azure Cognitive Services APIs, including:

• Vision APIs: Face detection, content tagging, and Optical Character Recognition (OCR)

• Language APIs: Language detection, sentiment analysis, and key phrase extraction

You’ll implement these services using both Python and JavaScript, ensuring a practical, real-world learning experience that prepares you for modern AI development tasks.

Detailed Course Content:

1. Analyze Solution Requirements (25–30%)

• Recommend and select Azure Cognitive Services APIs

• Choose appropriate data processing technologies and AI models

• Map security and automation needs to technologies and tools

• Align with data privacy, protection, and compliance regulations

• Identify software, services, and storage to support the AI solution

2. Design AI Solutions (40–45%)

• Create AI workflows and data ingestion/egress strategies

• Integrate pipelines using Azure Machine Learning and AI apps

• Build solutions using Vision, Speech, Language, and Knowledge APIs

• Design and integrate bots using the Microsoft Bot Framework and LUIS

• Select the right compute infrastructure (GPU, FPGA, CPU) and ensure cost-efficiency

• Incorporate governance, compliance, and security principles in AI design

3. Implement and Monitor AI Solutions (25–30%)

• Develop and manage AI pipelines and data flow

• Construct custom AI service interfaces and solution endpoints

• Integrate Azure Cognitive Services and the Microsoft Bot Framework

• Implement Azure Cognitive Search

• Monitor key performance metrics and optimize AI performance

Whether you are aiming to pass the AI-102 certification or seeking to apply AI concepts in your organization, this course will equip you with both theoretical understanding and practical expertise.

If you have any questions or need guidance, feel free to reach out. I’m here to support your learning journey.

Welcome to the course — let’s get started!