Data Engineering & DevOps
Data & DevOps
Build reliable data pipelines, manage infrastructure as code, and automate secure CI/CD across cloud and AI workloads.
Start with the first guideYour outcome
Ship repeatable AI platforms instead of one-off demos.
Core curriculum
The skills to practice
Focused topics
Build pipeline skills step by step.
CI/CD pipeline practices
Design repeatable build, test, release, and recovery stages around one trusted artifact.
Read topic guideInfrastructure as code (IaC)
Manage Terraform changes, state, modules, drift, and production approvals with clear boundaries.
Read topic guideReliable data pipelines
Build observable ingestion and transformation workflows that recover safely from late, duplicate, or invalid data.
Read topic guideGuided path
Cloud AI solution architect
Design secure landing zones, model gateways, data boundaries, evaluations, and cost-aware operating models.
Suggested pace: 6 weeks · Capstone: Enterprise AI reference architecture
0 of 5 complete
- 01
Choose the data and platform boundary
Assign clear ownership for ingestion, retrieval, serving, and governance.
- 02
Estimate cost per useful outcome
Build a workload estimate and identify meters to validate after deployment.
- 03
Provision with infrastructure as code
Define state, identity, review, and environment separation.
- 04
Build secure delivery gates
Add deterministic tests, evaluation thresholds, protected deploy, and rollback.
- 05
Present the architecture as evidence
Publish a concise case study with assumptions, trade-offs, and limitations.
Practical guides
Learn through decisions and builds.
Provision Microsoft Foundry with Terraform
Use AzureRM Terraform to create a Microsoft Foundry account and project, secure remote state, authenticate without stored secrets, and review deployments safely.
Where Databricks fits in a generative AI platform
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 GenAIOps pipeline with GitHub Actions
Build a practical GitHub Actions release path for AI applications, evaluation suites, Terraform, and secure Azure deployments using workload identity federation.
Build a CI/CD pipeline that is safe to release
Design a practical CI/CD pipeline with fast feedback, immutable artifacts, security boundaries, protected environments, deployment checks, and a tested recovery path.
Infrastructure as code: plan changes with confidence
Learn the operating practices behind maintainable infrastructure as code: clear ownership, reusable modules, protected state, reviewed plans, drift handling, and safe environment promotion.
Design reliable data pipelines from source to consumer
Build trustworthy data pipelines with explicit contracts, idempotent recovery, schema validation, access controls, lineage, and freshness monitoring for analytics and AI workloads.