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Python AI & Cloud Computing

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The course focuses on applied AI and cloud-based machine learning, equipping students with hands-on skills in model training, AI agent development, and cloud deployment for careers at the forefront of modern tech.

Build a data pipeline that runs in the cloud and uses both a machine learning model you trained and a large language model (LLM) to add meaning to the data.

What you’ll learn

This is course 3 of 4 in the Data Engineering track: Intro to Programming → Python for Data Analysis → Python for AI & Cloud → Python & Data Practicum.

  • Week 1: Production Python. Write code like professional teams: classes, type hints, data validation, automated tests, and packages.
  • Weeks 2-4: Machine learning. Train and evaluate regression and classification models, choose which one to use, and package it as a component.
  • Weeks 5-7: AI. Learn how LLMs work, write effective prompts, and build RAG systems and AI agents.
  • Weeks 8-11: Cloud and pipelines. Learn core cloud concepts, load data into a cloud database, combine models and LLMs in a pipeline, and learn basic AWS.
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What you’ll build

A cloud data pipeline that uses machine learning and AI, using daily weather data.

  • Train models with scikit-learn to predict the weather and classify days. Turn the best model into a reusable, tested Python component.
  • AI tools. Build a chatbot that answers questions using outside documents (retrieval-augmented generation, or RAG) and aI agents that can use tools.
  • ETL pipeline. Build an extract-transform-load (ETL) pipeline that pulls a year of weather data from a public API into a Supabase cloud database. It labels each day with your model, then uses an LLM to write a short recommendation for each day.

Tools you’ll use

Python, scikit-learn, Pydantic, pytest, OpenAI API, LlamaIndex, smolagents, Supabase, SQL, Prefect, AWS, git & GitHub.

Prerequisites

You must complete Python for Data Analysis first. You should be comfortable with:

  • Loading, cleaning, and analyzing data with Pandas
  • Writing SQL queries, including joins and aggregations
  • Getting data from APIs
  • Git and GitHub, including pull requests

Supplies

  • A laptop or desktop computer with at least 100GB of free disk space and 16GB of RAM. Chromebooks and similar devices do not work.
  • A webcam for live mentor sessions (preferred, not required)
  • Reliable internet
  • VS Code
  • A free GitHub account