Overview

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Getting generative AI into production and keeping it reliable requires engineering-grade deployment and monitoring systems. This course teaches you to build the infrastructure enterprise GenAI solutions depend on. You'll design CI/CD pipelines using GitHub Actions or Azure DevOps with automated evaluation gates, environment promotion, and rollback triggers for prompt, model, and data updates. You'll implement rollout strategies, including blue/green deployments, A/B traffic splits, and canary releases using Azure API Management with per-team quotas and token-based rate limiting. The course covers orchestrator deployment using managed online endpoints, Azure Container Apps, and Foundry Agent Service. You'll also build observability architectures with Foundry Observability, Azure Monitor, and Application Insights for distributed tracing, latency dashboards, and token tracking. Finally, you'll learn LLM monitoring techniques to detect hallucinations, groundedness degradation, and semantic drift, and investigate prompt drift, data staleness, and model regression.

Syllabus

CI/CD Pipelines: Architecture and Evaluation Gates
- This module teaches you to design CI/CD pipeline architectures for AI solutions with checks for quality and safety before environment promotion, plus post-deployment monitoring signals that detect regressions after release.
CI/CD Pipelines: Multi-Asset Pipelines and Dependency Management
- 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.
Deployment Ops: Azure API Management as AI Gateway
- 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.
Deployment Ops: Progressive Rollout Strategies
- This module teaches you to design and implement progressive rollout strategies that enable safe model updates with automated rollback capabilities.
Orchestrator Deployment: Platform Selection
- This module teaches you to evaluate orchestrator deployment options and select the appropriate platform based on latency, cost, scalability, and operational requirements.
Orchestrator Deployment: Versioning and Dependencies
- This module teaches you to design versioning schemas and deployment processes that manage the complex dependencies between orchestrators and their dependent assets.
Observability: Architecture and Distributed Tracing
- 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.
Observability: Dashboards, Alerts, and Continuous Evaluation
- This module teaches you to configure production dashboards, set up continuous evaluation on live traffic, and implement alerting for quality and safety metrics.
LLM Monitoring: Hallucination and Quality Degradation Detection
- This module teaches you to design monitoring strategies that detect LLM-specific quality issues and systematically identify root causes for degradation.
LLM Monitoring: Continuous Evaluation Pipelines
- Configure continuous evaluation pipelines that provide ongoing quality assessment of production LLM systems with alerting and investigation integration.
Project Module: Enterprise Deployment & Monitoring Architecture
- 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.