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Capstone: Build and Operate an AI-Native Data Platform

IBM via Coursera

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Details

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

9 weeks, 3 hours a week
Sessions

Self-Paced
Level

Intermediate
Subtitles

English

Found in

Part of

IBM AI-Native Data Engineering

Overview

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The capstone integrates all prior learning into a production-style portfolio project. Learners define a realistic AI-native data engineering use case, translate business and AI requirements into architecture decisions, ingest and model data, implement core platform components, and build at least one AI-native workflow such as vector retrieval, RAG, governed unstructured data preparation, or a reproducible training-data release. The project also requires governance, validation, documentation, and operations planning. By the end of the course, learners produce a portfolio-ready end-to-end AI-native data platform that demonstrates architecture, implementation tradeoffs, observability plans, CI/CD support, quality controls, access and governance decisions, and incident-response readiness. The capstone is designed to show applied, enterprise-relevant engineering judgment rather than only isolated technical tasks.

Syllabus

Welcome to the Course
- This welcome module orients you to Course 7 as the capstone experience of the AI-Native Data Engineering Professional Certificate. You will review the course purpose, expected outcomes, prerequisites, and the professional value of building a reliable, governed, portfolio-ready AI-native data platform.
Capstone Use Case, Requirements, and Success Criteria
- 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.
Source Assessment, Architecture, and Data Product Design
- 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.
Ingestion, Storage, and Core Pipeline Implementation
- 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.
AI-Native Component and Serving Interface
- 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.
Quality, Evaluation, Governance, and CI/CD
- 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.
Operations, Monitoring, Incident Response, and Portfolio Presentation
- 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.
Final Exam
- 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.
Course Summary
- This short closing module celebrates your completion of Course 7 and the full AI Native Data Engineering Professional Certificate. You will reflect on the broad professional capabilities you developed across the capstone and identify ways to keep applying and strengthening those skills in real world practice.