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