
Present the course outline and learning recommendations for the AI901 exam, detailing two core capabilities: identify AI concepts and implement AI solutions with Microsoft Foundry, and include hands-on labs.
Explore machine learning, predictive AI, and generative AI, clarify AI versus ML and supervised versus unsupervised learning, and highlight Azure Predictive AI and Microsoft Foundry.
Explore how predictive AI and generative AI complement each other to solve business problems, such as predicting baggage carousel maintenance and flight timeliness.
Deploy a GPT 4.1 chat completion model in Microsoft Foundry via the model catalog, configure a global standard deployment, apply content guardrails, and explore the chat playground with custom prompts.
Learn to craft precise prompts for AI agents by focusing on goal, context, expectations, and source. Narrow, well-engineered prompts boost accuracy across language generation, code, and image tasks.
Master the Microsoft Foundry SDK by creating an AI project client to connect to a Foundry resource using endpoint and credentials, and instantiate sub-clients for OpenAI, Anthropic, or Google Gemini.
Perform a hands-on lab deploying an OpenAI model via the Foundry SDK, connecting to a GPT 4.1 deployment, authenticated with Azure CLI, and running a code-first OpenAI client.
Deploy a GPT image 1 model in Microsoft Foundry and explore the model catalog in the image generation playground. Tweak settings and prompts with reference images to practice prompt engineering.
Explore ai agents and compound ai systems that extend large language models with apis and external tools to automate workflows through dynamic runtime planning.
Create your first Foundry agent in portal, select a foundation model, enable versioning, attach tools like web search and code interpreter, then review traces for tool calls.
Learn to build a code interpreter enabled Foundry agent in code-first workflow, read a csv of products, run python code in a sandbox, and output a column chart of prices.
Explore Azure language service capabilities via a foundry resource, including language detection, key phrase extraction, sentiment analysis, named entity recognition and entity linking, plus personally identifiable information detection and redaction.
Perform sentiment analysis, language detection, keyphrase detection, named entity recognition, linked entities, and PII recognition using Azure language service in a hands-on lab with a Python notebook.
Explore the Azure Translator Service Playground in Microsoft Foundry, test text and document translation across English, Arabic, and French, and call the service via SDK or REST API.
Introduces the features and capabilities of the Azure Computer Vision service (Azure AI Vision) - a Foundry tool - with caption and tag generation, object detection, and OCR.
Explore how image processing uses pixels, grayscale and RGB colors, and how 3x3 kernels modify brightness, blur, edge detection, and train a convolutional neural network for object detection.
Build an audio-enabled JNI app using a multimodal GPT audio model on Azure OpenAI, deploying the model, configuring environment variables, and sending MP3 audio input via base64 encoding.
Discover how Azure AI Content Understanding processes unstructured data from invoices, PDFs, images, audio, and video, using analyzers and standard and pro modes for structured insights and compliance checks.
Explore Azure AI Content Understanding in the Microsoft Foundry portal, using the Content Understanding Studio to test document, audio, image, and video analyzers and inspect json responses for integration.
Learn to use the prebuilt Azure AI Content Understanding Read API in a code-first lab to analyze an invoice and handwritten image, with environment setup, async analysis, and markdown results.
Explore red teaming in Microsoft Foundry, using evaluation tools and attack strategies to test agent safety across risk categories, with synthetic data, attack inputs, and attack success rate analysis.
Welcome to the course!
Artificial Intelligence is rapidly transforming the technology landscape, and Microsoft Azure is making AI more accessible than ever through powerful cloud-based AI services, Generative AI capabilities, and AI agent frameworks. This course is designed to help you build a strong foundation in Artificial Intelligence while also preparing you for the AI-901 certification exam.
In this course, we will begin with the fundamentals of AI and Responsible AI principles including fairness, privacy, security, transparency, and accountability. You will also understand common AI workloads and how AI models are deployed and used in real-world scenarios.
From there, we will move into modern Generative AI concepts using Microsoft Foundry. You will learn about prompt engineering, AI models, AI agents, chat applications, and how Microsoft is enabling developers to build intelligent AI-powered solutions in a secure and governed environment.
The course also covers practical AI workloads involving text analytics, sentiment analysis, speech recognition, speech synthesis, computer vision, image generation, multimodal AI, and Azure AI Content Understanding.
This course is beginner-friendly and structured in a simple and easy-to-follow manner with conceptual explanations, demonstrations, and hands-on learning. Whether you are a student, developer, analyst, cloud professional, or someone curious about AI, this course will help you confidently begin your Azure AI journey and prepare for the AI-901: Microsoft Azure AI Fundamentals certification exam.