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Unstructured Data Engineering for AI

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

Data Ingestion Courses

Part of

IBM AI-Native Data Engineering

Overview

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This course prepares learners to engineer governed unstructured data pipelines for AI training, semantic search, and RAG systems. Learners design ingestion workflows for documents and multimodal content, apply extraction and OCR-aware processing, normalize text while preserving useful structure, detect sensitive information, create chunking strategies, and enrich corpora with metadata for citation, filtering, lineage, and governance. By the end of the course, learners can build or specify unstructured ingestion workflows, evaluate extraction quality, prepare corpora for embedding or training, and apply safety and quality gates for PII, bias, source trust, and licensing risk. The course emphasizes corpus quality and governance so learners can produce reliable data assets for downstream AI systems.

Syllabus

Welcome to the Course

This welcome module introduces Course 5 and explains why unstructured data engineering matters for AI-native data work. Learners will orient themselves to the course’s professional value, expected preparation, and where to find the full course roadmap before beginning the technical modules.

Module 1: Unstructured Ingestion and Object Storage Architecture

Learn how to design a governed object storage foundation for unstructured AI datasets, including storage zones, naming conventions, manifests, and file quality controls. By the end of the module, you will be able to prepare an ingestion-ready corpus that supports downstream extraction, governance, lineage, and auditability.

Module 2: Content Extraction: Documents, HTML, OCR, and Structure

Learn how to extract text and preserve useful structure from PDFs, Word files, HTML, Markdown, and scanned documents while deciding when OCR is needed. You will build an extraction workflow, add quality checks and routing logic, and package audit-ready outputs for downstream cleaning, chunking, and governed AI use.

Module 3: Text Cleaning, Normalization, and Sensitive Data Handling

Learn how to turn extracted unstructured text into cleaner, safer, and more reliable corpus content for downstream AI workflows. You will inspect and normalize noisy text, remove boilerplate without losing important structure, and apply sensitive data detection, redaction, and audit practices that support governed corpus release.

Module 4: Chunking and Metadata Enrichment

This module teaches learners how to turn cleaned unstructured content into AI-ready chunks enriched with metadata for traceability, filtering, citation readiness, governance, and auditability. Learners compare chunking strategies, define chunk schemas, enrich records with source and governance metadata, and validate chunk quality before downstream embedding, retrieval, or training workflows.

Module 5: Annotation, Labeling, and Corpus Governance

This module teaches learners to design governed annotation and labeling workflows for unstructured AI corpora, from taxonomy creation through human review, agreement checks, AI-assisted labeling boundaries, versioning, and governance metadata. By the end, learners will be able to produce auditable labeling artifacts that improve corpus quality, reproducibility, and downstream AI readiness.

Module 6: Safety Gates, Quality Gates, and Course Project

This module brings together artifacts from earlier modules to evaluate whether an unstructured corpus is truly ready for AI use. You will define and apply safety, quality, licensing, and source trust gates, interpret validation results, and package a governed corpus release with clear evidence and a professional final report.

Course Summary

This short wrap-up module closes Course 5 by helping learners reflect on the value of governed unstructured data engineering for AI and recognize the progress they have made. It also previews how Course 6 builds on these themes without introducing new technical content.

Final Exam

This Final Exam assesses your ability to apply the full unstructured data engineering workflow for AI, from ingestion and extraction to normalization, chunking, labeling, and governed release decisions. You will demonstrate both core knowledge and practical judgment about traceable, reproducible, and risk aware corpus pipeline design.