Specialist Training
Microsoft Azure AI Engineering Course
Learn to build and operate AI workloads on Microsoft Azure. Connect Azure Machine Learning workflows with Microsoft Foundry, Azure OpenAI, retrieval and tool-using agents, then add deployment controls, identity, evaluation and monitoring.
- ·Choose it when you need a practical Azure implementation path for ML, LLMs or agents.
- ·For engineering principles across platforms, compare the MLOps and LLMOps courses below.
At a glance
- Focus
- Microsoft Azure AI implementation
- Working stack
- Azure ML, Microsoft Foundry and Azure OpenAI
- Project
- An Azure AI service with evaluated releases
- Starting point
- Python, APIs, Git and basic containers
Who Should Take This Microsoft Azure AI Course?
This course is for developers, ML engineers and platform teams building AI workloads in the Microsoft ecosystem. It is relevant when you need to connect an application, model deployment and agent workflow to Azure identity and operational controls.
Be comfortable with Python, APIs and Git. Basic model evaluation and container concepts are helpful. Azure resource setup and permission checks are part of the implementation work.
If you need programming and AI foundations first, explore the AI Engineering Course.
Microsoft Azure AI Engineering Syllabus
01Set up the Azure AI workload
Subscriptions, resource groups, Microsoft Entra identities, role assignments and managed identities. Establish storage, secret handling, region checks and cost controls for the lab.
02Build the Azure Machine Learning lifecycle
Run a small training job, record its configuration and evaluation, register the model and deploy a managed online endpoint. Connect pipeline stages to a release decision.
03Build with Microsoft Foundry and Azure OpenAI
Work with model deployments, application requests, quotas and evaluation. Microsoft Foundry is the current platform name; learners may also encounter Azure AI Foundry in older material.
04Add retrieval and a bounded agent workflow
Use Azure AI Search for retrieval over a controlled document set. Introduce Foundry Agent Service for a workflow with a limited tool, clear identity and observable execution.
05Apply release and monitoring controls
Use an evaluation check before deployment, keep application configuration versioned and connect application telemetry to Azure Monitor and Application Insights. Practice handling failed requests and rolling back a release.
06Compare managed deployment and AKS
Compare Azure ML managed endpoints, managed model access and container serving on AKS. Examine KServe as an architecture option, including operational responsibility, scaling, networking and GPU requirements.
Build an Azure AI Service with Release Evidence
Use one bounded support workflow, developed through three checkpoints:
ML endpoint.
Train and register a small ticket-classification model in Azure ML, then deploy it with a recorded evaluation result.
Knowledge application.
Build a document assistant using Azure OpenAI and Azure AI Search. Retain retrieval evidence and a repeatable answer-evaluation set.
Controlled agent action.
Add a Foundry Agent Service workflow that calls a limited test tool. Capture identity boundaries, traces and failure behavior.
Project artifacts: deployment code, resource architecture, evaluation results and a runbook covering access, release, rollback and resource cleanup.
Review focus: service selection, resource boundaries and evidence that the application can be evaluated and operated. The AKS comparison is an architecture walkthrough, keeping the hands-on project focused.
Choose Azure Specialization or a Broader Operations Course
Implement ML, LLM and agent workloads on Microsoft Azure
This Microsoft Azure AI course
Learn the complete ML lifecycle beyond one cloud implementation
MLOps CourseGo deeper into LLM evaluation, serving, reliability, RAG and agent operations
LLMOps CourseStudy ML, LLM and agent operations together in SCAI's broader program
AIOps CourseFor the corresponding AWS implementation, see AWS AI Engineering. Self-hosted inference and the broader LLM lifecycle remain part of the existing LLMOps Course linked above.
Training details
Microsoft Azure AI Course Syllabus, Fees and Training Options
Request the syllabus and current training options for Microsoft Azure AI Engineering. Share your role and whether you want to focus on Azure ML, Azure OpenAI or agent workloads. The team will confirm delivery format, schedule, fees and Azure access requirements before enrolment.
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