Practical guidance for systems that need to work.

Design a practical CI/CD pipeline with fast feedback, immutable artifacts, security boundaries, protected environments, deployment checks, and a tested recovery path.

Learn the operating practices behind maintainable infrastructure as code: clear ownership, reusable modules, protected state, reviewed plans, drift handling, and safe environment promotion.

Build trustworthy data pipelines with explicit contracts, idempotent recovery, schema validation, access controls, lineage, and freshness monitoring for analytics and AI workloads.

Build a portfolio-ready, citation-backed knowledge assistant with a seven-day plan for retrieval, grounding, evaluation, security, and documentation.

Assess 20 practical AI engineering skills across software, AI applications, retrieval, agents, evaluation, and operations. Get a private, saved-on-device learning plan.

A practical 12-week roadmap for turning cloud experience into production AI engineering skills, three portfolio projects, and credible career evidence.

Map the AI-103 objectives to an eight-week plan of Microsoft Foundry labs and portfolio-ready projects.

Design retrieval, citations, evaluation, and security for a useful Azure-based knowledge assistant.

Understand Model Context Protocol, its role in agent tooling, and the decisions that matter in Azure deployments.

Create an evidence-based evaluation loop for tool-using agents with quality, safety, latency, and cost measures.

A decision guide for subscriptions, identity, networking, policy, data boundaries, and observability in an AI platform.

Turn informal prompts into versioned, testable application assets with clear inputs, outputs, and failure behavior.

Use AzureRM Terraform to create a Microsoft Foundry account and project, secure remote state, authenticate without stored secrets, and review deployments safely.

Compare sequential, concurrent, handoff, supervisor, and group collaboration patterns with practical trade-offs.

Estimate and control Azure AI workload costs with unit economics, model and search meters, application guardrails, and Azure Cost Management alerts.

Decide where Azure Databricks fits in a generative AI architecture, when to pair it with Azure AI services, and how to govern, evaluate, and operate the system.

Build a practical GitHub Actions release path for AI applications, evaluation suites, Terraform, and secure Azure deployments using workload identity federation.

Practice explaining Azure connectivity, private endpoint DNS, hub-spoke routing, hybrid paths, and troubleshooting with eight interview scenarios and answer frameworks.

Build a concise cloud architect portfolio with three evidence-rich case studies, architecture decisions, security boundaries, operational plans, and honest project outcomes.

Design layered safeguards for agent identity, untrusted retrieved data, tool permissions, consequential actions, monitoring, and recovery.

Choose a Microsoft Foundry model by task fit, representative evaluations, region and deployment availability, data requirements, latency, and total workload cost.

Replace supported Azure application secrets with managed identities, then map each workload-to-service permission, role scope, network rule, and diagnostic test.

Decide whether prompting, retrieval-augmented generation, fine-tuning, or a combination best addresses changing knowledge, repeatable behavior, evaluation, and operating constraints.

Turn AI and cloud experience into concise resume evidence: your ownership, the architecture decision, measurable scope, verified outcomes, and links to credible project work.

Design agent memory by separating conversation context, workflow state, user preferences, and external knowledge, with explicit access, retention, deletion, and evaluation rules.