# AINS6006: Big Data Management for AI Applications

**Aurnova MSAI track:** Core  
**Credits:** 3  
**Format:** 8-week online graduate course

Designs data platforms, pipelines, lineage, cloud integration, and security for AI workflows.

This course follows the Aurnova/Castalia course-site pattern used by AINS6003: each module includes book prose, an assignment notebook, slide notebook, narration, instructor notes, and an executable lab.

## Course Outcomes

By the end of the course, students will be able to:

- explain the major concepts and tradeoffs in Big Data Management for AI Applications;
- build or evaluate applied AI artifacts aligned with the course domain;
- document assumptions, evidence, limitations, and operational risks;
- connect technical work to governance, stakeholder needs, and deployment readiness.

## Module Map

1. **Data architectures for AI** — What architecture supports trustworthy AI workflows?
2. **Pipelines, orchestration, and quality** — How do data pipelines fail, and how are failures detected?
3. **Storage, indexing, and retrieval** — How do access patterns shape storage choices?
4. **Distributed processing and scale** — When does scale require distributed computation?
5. **Metadata, lineage, and provenance** — How do we preserve the story of data transformations?
6. **Cloud integration and cost control** — How do cloud choices affect reliability and budget?
7. **Security and access governance** — How should sensitive data be protected across AI workflows?
8. **AI data platform readiness review** — What proves a data system can support production AI?