This module teaches you to design CI/CD pipelines that handle the multiple asset types in AI solutions, with separate promotion tracks and dependency management to ensure coordinated, safe deployments.

This module teaches you to design and configure Azure API Management as a centralized AI gateway that provides enterprise-grade controls for Azure OpenAI deployments.

This module teaches you to design and implement progressive rollout strategies that enable safe model updates with automated rollback capabilities.

This module teaches you to evaluate orchestrator deployment options and select the appropriate platform based on latency, cost, scalability, and operational requirements.

This module teaches you to design versioning schemas and deployment processes that manage the complex dependencies between orchestrators and their dependent assets.

This module teaches you to design GenAI observability architectures with distributed tracing that provides visibility across the complete request path from user to model and back.

This module teaches you to configure production dashboards, set up continuous evaluation on live traffic, and implement alerting for quality and safety metrics.

This module teaches you to design monitoring strategies that detect LLM-specific quality issues and systematically identify root causes for degradation.

Configure continuous evaluation pipelines that provide ongoing quality assessment of production LLM systems with alerting and investigation integration.

In this project, you'll design a complete enterprise deployment and monitoring architecture for a production GenAI platform. You'll create CI/CD pipeline designs with evaluation gates, configure AI gateway and progressive rollout strategies, design observability architecture with LLM-specific monitoring, and produce a deployment operations runbook—demonstrating your ability to operationalize enterprise AI at scale.