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Learn at your own pace with high-quality pre-recorded lessons. Access anytime, pause, rewind, and rewatch — on any device.
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The project automates procurement document validation by building a pipeline in Databricks using LLMs, embeddings, Delta Lake, and Unity Catalog
Organizations typically receive Purchase Orders (POs), Goods Receipts (GRs), and Invoices as PDFs from multiple vendors. Stored in unstructured formats and handled manually, this creates four recurring pains:
There's a need for an automated, scalable, and intelligent system that extracts, validates, and reconciles procurement data — using modern AI/LLM, vector search, and Delta Lake.
The project automates procurement document validation by building a production-grade pipeline in Databricks using LLMs, embeddings, Delta Lake, and Unity Catalog.
PDF ingestion via UC Volumes
LLM-based text extraction
Embeddings & Vector Search
Delta Lake Bronze→Silver→Gold
PO ↔ GR ↔ Invoice matching
Confidence scoring & exceptions
Unity Catalog governance
Databricks Workflows
Monitoring dashboards
Deployment & demo walkthrough
A guided, phase-by-phase walkthrough of the procurement automation project — from raw PDF ingestion to a deployed pipeline with monitoring. Every module includes source code, notebooks, and architecture explanations.
Learn at your own pace with high-quality pre-recorded lessons. Access anytime, pause, rewind, and rewatch — on any device.
A curriculum designed by industry experts to take you from first principles to production-grade competence.
Join an exclusive cohort of ambitious engineers. Network, collaborate on projects, and build career-shaping connections.
Stuck on a bug or concept? Post in the chat groups and get help from peers and instructors — fast.
Reinforce what you learn with assessments, live quizzes, and project-based evaluations you can track over time.
Earn a shareable certificate on completion. Add it to your LinkedIn profile with a single click.
What past learners say about working through the program.
Most "Gen AI" courses stop at a toy RAG demo. This one actually shows you how to wire LLMs, Vector Search, and Delta Lake into a pipeline that runs in production. We adapted the pattern for invoice reconciliation at our company the very next quarter.
The three-way match logic and confidence scoring design alone were worth the price. Clear architecture, clear code.
Loved seeing every piece — ingestion, extraction, embeddings, matching, orchestration — stitched together. My resume now reads very differently.
I needed a real LLM-on-Databricks project to put on my portfolio. This delivered exactly that — and I got interview calls within weeks.
Architecture decisions are explained, not just coded. That's rare. Now I know why we picked Vector Search over a join.
Quick answers to common questions. Can't find what you need? Drop us a note — we'll reply within 24 hours.
Ask a questionData engineers, analytics engineers, and AI practitioners who want a realistic, production-grade end-to-end project on Databricks. You'll get the most out of it if you're already comfortable with PySpark, Delta Lake, and basic Python.
Basic Databricks familiarity is strongly recommended — if you're new, pair this with the Databricks Zero to Hero course first. LLM experience is not required; we cover prompt engineering, model serving, and embeddings from first principles.
Yes. The full codebase, sample PDFs, and notebooks are included. You can run the project on a Databricks trial workspace with Unity Catalog enabled. Foundation models are accessed via Databricks Model Serving (free tier available for light usage).
Absolutely — we encourage it. You'll learn not just how to build it, but how to talk about the architecture in interviews. We also share a one-page project summary template you can adapt for your resume.
Yes. The course is fully pre-recorded. Watch at your own pace, pause, rewind, and revisit modules as needed. Lifetime access is included.
Yes. Once you complete all phases and the final demo, you receive a verified GeekCoders project certificate you can share on LinkedIn in one click.
7-day no-questions-asked refund window from the date of purchase. See our refund policy for full terms.