Model Online / On-Site
Duration
Modules 5 modules

Course Objective & Scope

Most enterprise knowledge lives outside relational tables—inside contracts, technical manuals, call recordings and images. Feeding those files raw into an LLM or vector database leads to mangled tables, lost heading hierarchies and poor retrieval quality. This programme teaches how to engineer production-grade pipelines for unstructured multimodal data.

Layout-aware document parsing with Docling and Unstructured, reading complex tables and figures with OCR and vision-language models, audio transcription and speaker diarization with Whisper, semantic chunking, metadata extraction and incremental indexing are covered hands-on. Participants build an end-to-end ingestion pipeline on sample files from their own organisation along with an AI-ready quality checklist.

Who Should Attend?

  • AI and data engineers building RAG, enterprise search and document intelligence systems
  • Software developers processing call-centre, legal, financial and technical documentation data
  • Data scientists preparing multimodal datasets
  • Prerequisite: Python programming and basic API/data processing experience

Key Takeaways

  • Parse PDF, DOCX, HTML and scanned documents while preserving headings, tables and layout
  • Convert audio recordings into timestamped, speaker-attributed text segments via ASR and diarization
  • Caption images, charts and diagrams with multimodal models and link them to surrounding text
  • Apply document-aware chunking, metadata enrichment and PII redaction
  • Track file changes and update vector and search indexes incrementally

Curriculum

Unstructured Data Architecture and Storage
  • Object storage (S3/MinIO) layout, content hashing and version tracking
  • Batch and event-driven ingestion patterns
Layout-Aware Document and Table Parsing
  • Reading PDFs, tables and forms with Docling, Unstructured and OCR/VLMs
  • Markdown/JSON intermediate representations and cleaning extraction artifacts
Audio and Image Processing Pipelines
  • Speech-to-text (ASR) with Whisper, speaker diarization and timestamped segmentation
  • Extracting summaries and metadata from charts, photos and diagrams with VLMs
Chunking, Metadata Enrichment and PII Redaction
  • Hierarchical, table-aware and semantic chunking strategies
  • Automated domain/date/ACL metadata extraction and PII scrubbing
Embedding, Indexing and Quality Validation
  • Idempotent writes to vector and hybrid search (BM25 + dense) indexes
  • Parsing quality metrics and quarantining corrupted files

Other Courses in This Category

  • The Modern Data Stack: dbt, Apache Airflow and Apache Iceberg

    Lakehouse storage on Apache Iceberg, modular tested transformations with dbt and orchestration with Apache Airflow, for engineers building scalable, open-standard data platforms.

    Data Science & AI-Ready Data Engineering Corporate
    Live Course
    5 modules
    View
  • AI-Ready Data Architecture

    Enterprise data architecture supporting analytics, classical ML, RAG and autonomous agents under one roof: designing lakehouse storage, semantic layers, feature stores, vector indexes and governance.

    Data Science & AI-Ready Data Engineering Corporate
    Live Course
    5 modules
    View
  • Automated Data Quality Testing for Analytics and AI

    Writing schema, business-rule, referential integrity and statistical distribution tests with dbt tests, Soda Core and Great Expectations to catch bad data in CI/CD and staging.

    Data Science & AI-Ready Data Engineering Corporate
    Live Course
    5 modules
    View
  • Data Contracts: Producer-Consumer Agreements and Schema Governance

    Practical training on securing data exchange between software producers and data/AI consumers using OpenDataContractStandard (ODCS), Schema Registry and automated CI/CD checks.

    Data Science & AI-Ready Data Engineering Corporate
    Live Course
    5 modules
    View
  • Data Lineage and Enterprise Data Catalogs

    Building an enterprise data catalog with OpenMetadata and DataHub, managing business glossaries, automating metadata ingestion and tracking column-level lineage with OpenLineage.

    Data Science & AI-Ready Data Engineering Corporate
    Live Course
    5 modules
    View
  • Data Observability: Monitoring, Drift Detection and Incident Management

    Detecting freshness, volume, schema and distribution anomalies across data pipelines, preventing alert fatigue, running root-cause analysis and managing data incidents for data and AI platform teams.

    Data Science & AI-Ready Data Engineering Corporate
    Live Course
    5 modules
    View

Get in touch