This lab is currently in Beta, content may be updated as we refine the material
LABBEGINNER

Python AI Development Environment

Set up a Python environment for AI development with ML frameworks and LLM clients.

45 minutes
ai-bootcamp
Python AI Development Environment - Platform Engineering Hands-On Lab Icon
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Lab Overview

🛠 Lab from the AI Platform Engineering Bootcamp. Used in Week 1. Bootcamp landing page: https://academy.tekanaid.com/bootcamps/ai-platform-engineering-bootcamp Parent course(s):

  • Week 1: AI Foundations for Infrastructure Engineers (slug: ai-foundations-101)

🟡 Beta bootcamp lab. Hands-on instructions, check scripts, and solve scripts are in place. Lab is part of the running Platform Assistant capstone arc that grows across all 8 weeks of the bootcamp.

In this lab, you'll configure a complete Python development environment for AI work.

You'll learn to:

  • Create and manage Python virtual environments
  • Install essential ML frameworks (PyTorch, NumPy, Pandas)
  • Set up LLM client libraries (OpenAI, Anthropic, LangChain)
  • Build a simple LLM client with streaming responses

Prerequisites:

  • Basic Python programming knowledge
  • Understanding of virtual environments

Key Concepts:

  • Python packaging and dependency management
  • ML framework installation patterns
  • Async programming basics for LLM clients

What You'll Learn

Create isolated Python virtual environments

Install and verify ML framework installations

Configure LLM client libraries

Build a basic LLM client application

Prerequisites

basic-python

command-line-basics

Technologies Covered

pythonaidevelopmentenvironmentllm

Part of a Course

This lab is part of the AI Foundations for Infrastructure Engineers course

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