This module helps learners define a realistic AI-native data engineering capstone before any architecture or implementation begins. Learners frame the use case, identify consumers and goals, set measurable SLOs and success metrics, and document risks and scope boundaries to create the first portfolio-ready capstone artifacts.

This module helps learners turn capstone requirements into a practical platform design by assessing source suitability, defining a curated data product, selecting an AI-native component path, and documenting key architecture decisions. Before implementation begins, learners produce evidence-based design artifacts that balance realism, governance, reliability, cost, and scope.

This module guides learners through building the core implementation of their capstone data platform, from repeatable ingestion and source aligned storage to curated transformations and orchestration. By the end, learners will have a documented, reviewable pipeline that produces a trusted data product ready for downstream AI native use.

This module guides learners through selecting and implementing one well-scoped AI-native component for their capstone, such as retrieval, RAG, a governed corpus, a feature pipeline, or a reproducible training dataset release. Learners then expose that component through a usable serving interface and document metadata, attribution, usage boundaries, failure modes, and value for consumers.

This module teaches learners how to make an AI-native data platform trustworthy, reviewable, and ready for operational handoff. Learners add quality checks, evaluation evidence, reproducibility controls, governance documentation, and CI/CD-style validation gates to produce a complete validation and governance package for their capstone.

This module prepares learners to operate and present their AI-native data platform like a production-aware capstone project. Learners create monitoring, incident response, RCA, recovery, lifecycle, and cost artifacts, then package and communicate the platform’s architecture, tradeoffs, and value in a polished final demo.

The Final Exam assesses your ability to apply the full capstone workflow for designing and operating an AI-native data platform. You will make defensible decisions across architecture, implementation, governance, operations, and portfolio presentation using realistic scenarios and evidence-based tradeoffs.