- Learning Path Libraries: This path is only available in the libraries listed. To access this path, purchase a license for the corresponding library.
- Cloud
- Data
- Security
Build a Generative AI Solution with Azure
### Move Beyond the Chatbox. Become an AI Engineer.
Transition from standard cloud development to **AI Engineering**. In this code-first, hands-on path, you will build a production-grade "Smart Support Ticket" system. You will architect a serverless backend that automatically ingests raw text, processes it with Azure OpenAI, and stores the structured results for analytics.
Learn to navigate real-world enterprise constraints by securely integrating with pre-existing AI resources and handling strict API rate limits.
Content in this path
Evolution
Build a production-shaped generative AI solution in Azure by applying senior-level architectural judgment to model integration, serverless orchestration, secure data handling, observability, and operational hardening across the Azure ecosystem.
Try this learning path for free
What You'll Learn
- 1. Architect a scalable, Python-based backend using Azure Functions and Cosmos DB to ingest and process data.
- 2. Programmatically integrate Azure OpenAI Service to transform unstructured text inputs into structured business insights.
- 3. Implement production-grade error handling (exponential backoff) to manage API quotas and rate limits robustly.
- This path is designed for experienced Azure practitioners who want to build, connect, and operate a generative AI solution using Azure OpenAI, Azure Functions, Azure data services, and application monitoring.
- Learners should be comfortable:
- Architecting Azure solutions that combine compute, storage, database, identity, networking, and monitoring services
- Building and troubleshooting event-driven workloads with Azure Functions or similar serverless patterns
- Working with Azure Blob Storage, Cosmos DB, secure application settings, and diagnostic logs
- Writing and modifying Python code that calls external APIs, handles errors, and persists structured data
- Reasoning about authentication, regional placement, rate limits, retries, observability, and operational resilience
- This path does not introduce Azure fundamentals, basic Python programming, or entry-level generative AI concepts. The content assumes senior engineer or architect-level judgment and focuses on building a production-shaped AI workflow that integrates model calls, data ingestion, structured storage, monitoring, and hardening controls across the Azure ecosystem.
- Azure OpenAI
- Azure Cosmos BD
- Retrieval-Augmented Generation
Learn with the best
