Module 2: Data handling with Python libraries

Module 2: Data handling with Python libraries#

AINS6007 — Applied AI Programming with Python

Essential Question#

How do Python tools support reproducible data work?

Scenario#

an engineering team converting prototype AI code into a maintainable application component

Stakeholders: software engineer, ML engineer, QA lead, product owner, and operations reviewer

Core Moves#

  • Define the decision boundary

  • Compare baseline and alternative

  • Interpret evidence and assumptions

  • Identify failure modes

  • Recommend next action

Lab & Assignment#

Load, validate, and transform a dataset.

Artifact: tested Python AI component with interface contract, CI evidence, and deployment notes focused on data handling with python libraries: Load, validate, and transform a dataset.