📚 Syllabus & Course Curriculum
Data Science & Analytics
Module-by-module breakdown of Microsoft Azure for AI Services, from foundations to a certified capstone project.
Platform
• Subscriptions, resource groups and role-based access control
• Networking, private endpoints and data residency choices
• Cost management and the levers that actually control AI spend
Prebuilt
• Vision, speech, language and document intelligence services
• Azure OpenAI Service: deployment, quotas and content filtering
• When a prebuilt service is sufficient and when it becomes a constraint
Custom
• Workspaces, compute targets and managed endpoints
• Pipelines, environments and model registry
• Responsible AI dashboard components for fairness and error analysis
Data
• Azure AI Search for retrieval-augmented generation
• Data ingestion, chunking and index maintenance
• Evaluating retrieval quality rather than assuming it
Operations
• Monitoring, logging and drift detection for deployed endpoints
• CI/CD for models with Azure DevOps or GitHub Actions
• Security posture, identity and compliance evidence for enterprise deployment
Earn government-registered certification in Microsoft Azure for AI Services
e-Certificate and e-Marksheet issued on successful completion.
