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AI Engineering with Python
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Module 1
Python Foundations for AI Applications
1
Creating a Reproducible Python Project on macOS
Create a reproducible Python project on macOS with an isolated environment, a pyproject.toml file, and pinned dependencies.
Create a reproducible Python project on macOS with an isolated environment, a pyproject.toml file, and pinned dependencies.
2
Transforming Nested Data with Python Collections and Comprehensions
Transform nested application data using Python collections, comprehensions, slicing, and unpacking.
Transform nested application data using Python collections, comprehensions, slicing, and unpacking.
3
Typed Functions and Configuration Dataclasses
Define typed Python functions and represent configuration data with dataclasses.
Define typed Python functions and represent configuration data with dataclasses.
4
Validating AI Inputs and Outputs with Pydantic Models
Validate structured AI inputs and outputs with Pydantic models.
Validate structured AI inputs and outputs with Pydantic models.
5
Reading and Writing Text, JSON, JSONL, and CSV Files with pathlib
Read and write text, JSON, JSONL, and CSV data using pathlib and context managers.
Read and write text, JSON, JSONL, and CSV data using pathlib and context managers.
6
Handling File and Network Failures with Targeted Exceptions and Cleanup
Handle file and network failures with targeted exceptions and cleanup logic.
Handle file and network failures with targeted exceptions and cleanup logic.
7
Testing Python Components with pytest Fixtures, Parameterization, and Mocks
Test Python components with pytest fixtures, parameterization, and mocks.
Test Python components with pytest fixtures, parameterization, and mocks.
8
Concurrent Model Requests with Asyncio Limits
Use asyncio to run multiple model-style requests under a concurrency limit.
Use asyncio to run multiple model-style requests under a concurrency limit.
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