Advanced Course
Azure Openai Advanced
49 lessons across 7 chapters. Every lesson is standalone — start anywhere.
49 lessons 7 chapters
1 Enterprise Azure OpenAI Architecture 7 lessons
1
Azure AI Foundry for enterprise Use Azure AI Foundry to deploy, monitor, and govern LLM applications across your organization with built-in compliance, cost tracking, and multi-tenant isolation.
2 Hub and project model Azure OpenAI's hub-and-project isolation model partitions API deployments, quotas, and audit logs for multi-team or multi-environment control.
3 Multi-region deployment for HA Route Azure OpenAI API calls across multiple regions with automatic failover to maintain availability when one region degrades or throttles.
4 Private endpoint configuration Configure Azure OpenAI clients to route traffic through private endpoints instead of public internet endpoints to meet network isolation requirements.
5 Content filtering policy management Configure and enforce Azure OpenAI content filtering policies to block, flag, or allow specific content categories in chat completions.
6 Cross-account governance Enforce tenant isolation and role-based access control across multiple Azure subscriptions when deploying Azure OpenAI models.
7 Landing zone for Azure OpenAI Initialize and authenticate the AzureOpenAI client to establish a secure connection to your Azure OpenAI deployment.
2 Azure OpenAI Compliance 7 lessons
1
HIPAA BAA for Azure OpenAI Azure OpenAI supports HIPAA-covered entities through Business Associate Agreements, but you must explicitly enable compliance features and understand what Azure does and doesn't cover under the BAA.
2 Data residency configuration Control where your prompts and completions are processed and stored by specifying Azure region and API version in the AzureOpenAI client.
3 Zero data retention setup Configure Azure OpenAI to disable data retention and immediately delete conversation logs by setting data_in_at_rest_encryption_enabled and using the correct API version.
4 SOC2 and ISO compliance Azure OpenAI enforces SOC2 Type II and ISO 27001 compliance through audit logging, data residency controls, and encryption: configure your client to capture and retain logs for regulatory proof.
5 GDPR Compliance and Data Residency Configure Azure OpenAI deployments in GDPR-compliant regions and implement request logging patterns that satisfy EU data residency requirements without exposing sensitive user data.
6 Azure Policy for AI governance Enforce compliance rules and audit AI API usage across your Azure OpenAI deployments using Azure Policy definitions and assignments.
7 Enterprise compliance documentation Extract and structure compliance audit trails from Azure OpenAI API calls to satisfy regulatory requirements without manual log parsing.
3 LangChain and LlamaIndex on Azure 7 lessons
1
AzureChatOpenAI in LangChain Use LangChain's AzureChatOpenAI to integrate Azure OpenAI deployments with chains, agents, and RAG pipelines while managing authentication and token streaming at scale.
2 AzureOpenAIEmbeddings in LangChain Generate vector embeddings from text using Azure OpenAI's embedding models through LangChain's abstraction layer, enabling semantic search and retrieval augmented generation at scale.
3 Azure OpenAI in LlamaIndex Use Azure OpenAI as the LLM backbone in LlamaIndex RAG pipelines with explicit deployment configuration and managed indexing.
4 Azure AI Search in LangChain Integrate Azure AI Search as a retriever in LangChain LCEL to enable hybrid semantic and keyword search over your documents.
5 Building a RAG Pipeline with Azure OpenAI and Cognitive Search Combine Azure OpenAI's chat completions with Azure Cognitive Search to retrieve and augment responses with your own documents in a single production pipeline.
6 LangSmith with Azure OpenAI Instrument Azure OpenAI API calls with LangSmith to trace, debug, and monitor LLM behavior in production.
7 Framework vs native Azure SDK Choose between LangChain/LlamaIndex abstraction layers and direct AzureOpenAI SDK calls based on control needs, latency requirements, and cost visibility.
4 Content Safety and Responsible AI 7 lessons
1
Azure Content Safety service Use Azure Content Safety to analyze text and images for harmful content categories before processing through your LLM pipeline.
2 Content filter categories and severity Azure OpenAI content filters flag harmful content across categories (hate, sexual, violence, self-harm) with configurable severity thresholds in the response.
3 Custom content policies Apply custom content filtering rules to Azure OpenAI API calls by configuring filtering policies at the deployment and request level.
4 Groundedness detection Use Azure OpenAI with prompt engineering and external knowledge verification to detect whether model responses are grounded in factual sources or hallucinated.
5 Protected material detection Use Azure OpenAI's content filtering to detect and block requests containing protected material like violence, hate speech, and sexual content before processing.
6 Indirect prompt injection detection Detect when user input contains adversarial prompts designed to override system instructions by analyzing message patterns and content boundaries before sending to Azure OpenAI.
7 Responsible AI dashboard Monitor content filtering decisions, token usage, and model behavior through Azure OpenAI's content filter metrics and structured logging endpoints.
5 Advanced Azure OpenAI Patterns 7 lessons
1
Multi-model routing on Azure Route requests to different Azure OpenAI model deployments based on prompt characteristics, latency requirements, or cost targets using conditional logic and fallback patterns.
2 Azure Functions with OpenAI Deploy serverless Python functions on Azure that call Azure OpenAI endpoints with proper identity-based authentication and cold-start optimization.
3 Logic Apps integration Trigger Azure OpenAI completions from Logic Apps workflows using HTTP connectors and managed identity authentication.
4 Azure OpenAI for enterprise RAG Use Azure OpenAI's chat completions with vector search to build retrieval-augmented generation systems that scale across enterprise deployments.
5 Semantic Kernel with Azure OpenAI Use Microsoft's Semantic Kernel to compose orchestrated AI workflows that chain Azure OpenAI calls with memory, plugins, and planning without building custom orchestration logic.
6 Azure Bot Service integration with Azure OpenAI Route Azure Bot Service conversations through Azure OpenAI using the AzureOpenAI client to build stateful, context-aware conversational agents.
7 Event-driven Azure OpenAI pipelines Build scalable request-response pipelines using Azure Event Grid to queue, deduplicate, and route Azure OpenAI API calls with automatic retry and dead-letter handling.
6 Performance Optimization 7 lessons
1
Streaming for latency reduction Use Azure OpenAI streaming to receive chat completions incrementally, reducing perceived latency by up to 70% compared to waiting for the full response.
2 Prompt caching savings Use prompt caching to reduce token costs and latency by storing frequently repeated system prompts and context on Azure OpenAI's servers.
3 Connection pooling Reuse HTTP connections across multiple API calls to reduce latency and improve throughput when making repeated requests to Azure OpenAI.
4 Async SDK usage Use AsyncAzureOpenAI to make non-blocking API calls that scale to hundreds of concurrent requests without threading complexity.
5 Retry strategy configuration Configure exponential backoff and maximum retry attempts to handle transient failures and rate limits in Azure OpenAI API calls.
6 Timeout tuning Configure socket, request, and retry timeouts on AzureOpenAI client to prevent silent failures and handle long-running completions without killing valid requests.
7 Load Testing Azure OpenAI Systematically measure throughput, latency, and cost under concurrent load against Azure OpenAI deployments to validate capacity and identify bottlenecks before production traffic.
7 Operations and Governance 7 lessons
1
Azure Monitor integration Stream Azure OpenAI API metrics and errors to Azure Monitor for production observability and cost tracking.
2 Azure Log Analytics for OpenAI Stream Azure OpenAI API calls and token usage to Log Analytics workspace for production monitoring, cost tracking, and compliance auditing.
3 Diagnostic settings Enable Azure Monitor integration to capture request/response logs, latency metrics, and token usage for your Azure OpenAI deployments.
4 Operational runbook: Production deployment and incident response Build production-grade error handling, monitoring, and failover logic for Azure OpenAI deployments that stay online.
5 Incident Response for Azure OpenAI Detect, log, and recover from Azure OpenAI API failures with structured error handling, retry strategies, and circuit breaker patterns to maintain service reliability.
6 Change management for model upgrades Safely upgrade your Azure OpenAI model deployments without breaking production by validating compatibility, managing rollback states, and coordinating deployment names across your stack.
7 Multi-team governance model Implement role-based access control and cost allocation across teams using Azure OpenAI's managed identity and subscription-level RBAC to prevent credential sprawl and enforce spending guardrails.