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Learn concepts, build a real AI SaaS product, and turn the project into credible proof of skill.
14
Modules
236+
Lectures
10
Build phases
8 wk
Plan
Outcomes
Explain AI systems through a real product
Build Python and FastAPI AI services
Implement semantic search and RAG
Extract structured fields from documents
Evaluate hallucination, retrieval, citations, cost, and latency
Present a portfolio-ready AI SaaS project
Curriculum
Every module maps to a product capability, implementation skill, or capstone milestone.
Module 0
Clear product direction, capstone scope, and focused learning path.
Module 1
Build an AI-ready FastAPI document upload service with tests and clean structure.
Module 2
Explain core AI, ML, embeddings, transformers, inference, hallucination, and RAG tradeoffs.
Module 3
Build a document text extraction and metadata pipeline.
Module 4
Build reusable AI service calls for summaries, classification, and extraction.
Module 5
Extract and store invoice, contract, policy, and proposal fields.
Module 6
Search documents by meaning and return relevant chunks.
Module 7
Ask questions over one or many documents with cited, grounded answers.
Module 8
Create the complete architecture blueprint, API contracts, schema plan, job model, and UI route map.
Module 9
Implement classifier, contract review, invoice approval, comparison, and checklist workflows safely.
Module 10
Build golden datasets, eval scripts, usage tracking, prompt regression checks, and a quality dashboard plan.
Module 11
Create the security model for document access, vector privacy, secure uploads, audit logs, and PII-aware processing.
Module 12
Create Docker, environment, health check, deployment, monitoring, README, and demo video plans.
Module 13
Turn the entire course into a portfolio-grade product with upload, extraction, search, RAG, workflows, evals, security, and deployment.
Open the dashboard, start with the first available module, and work through the capstone path with the product beside you.