Microsoft Certified: Azure AI Fundamentals (AI-900)

Organizations adopting Azure AI need a shared baseline before teams build, buy, or support AI-enabled services. This AI-900 course gives IT Directors a structured way to align technical and non-technical staff on Azure machine learning, computer vision, natural language processing, Cognitive Services, responsible AI considerations, and basic model workflows.

The course is a fit for junior Azure data admins, new IT professionals with Azure exposure, and adjacent roles that need to understand Azure AI capabilities without starting in advanced engineering. The full curriculum is about 19 hours per learner, so Training Managers can assign it over several weeks alongside operational work. For change management, it works best as a foundational rollout: establish vocabulary, introduce Azure AI services, then identify which team members should continue into deeper ML, data, or cloud engineering paths.

CBT Nuggets support for Playlists and Team Reporting helps Team Leads assign the AI-900 path consistently and monitor completion across the team.

IT teams complete this training to make Azure AI adoption more practical and less ad hoc. The course connects fundamentals to scenarios teams are likely to encounter when evaluating or supporting AI services in Azure.

• Standardize AI discussions: Build a common understanding of machine learning workloads, classification, regression, computer vision, NLP, and responsible AI considerations.
• Evaluate Azure AI services: Recognize when Azure Machine Learning Studio, AutoML, Cognitive Services, Custom Vision, or Form Recognizer fit a business workflow.
• Support prototype workflows: Follow examples that use Python, PowerShell, SDKs, REST APIs, and Docker containers to interact with Azure Cognitive Services.
• Reduce handoff friction: Help cloud, data, and operations staff better understand AI service inputs, outputs, endpoints, and model-related terminology before projects move to production teams.

Knowledge

• Core AI and machine learning workload types, including regression and classification.
• How Azure Machine Learning Studio and AutoML support basic model creation workflows.
• Common computer vision scenarios, including image analysis, facial recognition, Custom Vision, and OCR-related tasks.
• Natural language processing concepts and Azure language processing resources.
• Responsible AI and ethics considerations for machine learning and AI projects.
• Where Azure Cognitive Services, Form Recognizer, SDKs, REST APIs, and containers fit in an Azure AI environment.

Ability

• Create basic regression and classification pipelines at a foundational level.
• Explore and configure Azure ML Studio and AutoML workflows.
• Build and consume Custom Vision and Form Recognizer models in guided scenarios.
• Call Azure Cognitive Services APIs with Python and PowerShell.
• Perform computer vision tasks and image annotation using Azure services.
• Deploy Azure Cognitive Services in Docker containers for supported scenarios.