Legacy To Ai Modernizing Mainframe System For Cloud & Ai
 

Legacy To Ai : Modernizing Mainframe System For Cloud & Ai
Last updated 7/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 1h 33m | Size: 572 MB
Learn to read legacy mainframe logic, rebuild it as a real-time cloud API, then layer in machine learning and Claude AI
What you'll learn
Understanding mainframe architecture (COBOL, ClearPath, LINC/DMSII systems) Why legacy systems still run core banking/finance operations today
Stage 1: Legacy system assessment and mapping Stage 2: Migrating to cloud architecture (FastAPI-based services) Stage 3: Layering in AI/ML capabilities
End-to-end build: legacy rule-based fraud detection → hybrid rule + ML system → explainable AI-driven scoring
Students walk away with a working, runnable fraud-detection system they can showcase in a portfolio
Requirements
Programming Experience Required. Basic Computer Knowledge Required
Description
If you've spent your career on a mainframe, writing COBOL, LINC/EAE, or working inside a Unisys ClearPath and DMSII environment - you already understand systems that most cloud engineers never see. This course bridges that expertise into the world of cloud APIs and AI, using one real example from start to finish: a bank's daily transaction-limit fraud check.
You'll begin by decoding that rule exactly as it exists today, reading real LINC specs and COBOL copybooks, including the traps that catch most migrations: COMP-3 packed decimals, EBCDIC encoding, and REDEFINES clauses. Next, you'll rebuild it as a production-grade FastAPI service complete with authentication, error handling, and load testing and watch response time drop from overnight batch to under 100 milliseconds.
Then you'll go further than the original rule ever could, layering in an XGB oost machine learning model and Claude AI-generated explanations that tell a fraud analyst exactly why a transaction was flagged.
By the end, you'll have a complete, working capstone project rules engine, ML, and AI explainability, wired together with a real feedback loop and a portfolio piece that proves you can lead modernization, not just talk about it.
- Module 0 - Why This Matters (budget/business case, Strangler Fig)
- Module 1 - Legacy: ClearPath, LINC, DMSII, COBOL copybooks + lab decoding a new LINC/copybook pair into a JSON schema
- Module 2 - Cloud: FastAPI rebuild, security, error handling, load testing + lab building and Locust-testing the limit-check endpoint
- Module 3 - AI: rules vs. ML, XGB oost, Claude explainability, feedback loop + lab adding the intelligence layer on top of Module 2's API
- Module 4 - Capstone: full four-layer system, testing strategy, golden dataset, complete build guide with code + GitHub README template
Who this course is for
Product Managers
Individual Looking for Modernization of Legacy System
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