Last week we deployed a cheap model on Azure Foundry and tested it with a simple prompt. This week we are going to put it to use: we are going to scrape the menu data from our university cafeteria ...

At the beginning of the semester we activated our student perks for several cloud platforms, including Microsoft Azure, GitHub and Google Cloud. We can put our $100 Azure credits to work in Microso...

Before we can start working on our projects this semester, we need to make sure that our systems are ready. This guide will help you set up your system with the necessary tools and software. Di...

If you have decided to continue with this semester’s Human-AI Interaction in Data Science course after the the orientation session, make sure to follow and complete the steps below before next clas...

In the previous lecture we scraped web content, sliced it into bite-sized pieces and stored the chunks plus their semantic embeddings in a vector database. Now it’s time to feast on that data using...

Get our local project ready After working with development containers in GitHub Codespaces for a bit, let’s return back to our own systems and use a local development environment for this project....

After today’s presentations, and in preparation for our next class, we’ll explore how to use MCP servers in Visual Studio Code. We’ll focus on the Context7 server by Upstash, which gives our AI co...

This week, let’s put our Google Cloud API credits to use. We’ll explore the Gemini API, Google’s large language model (LLM), and we’ll do so in GitHub Codespaces, a cloud-based development environm...

This week let’s activate your Google Cloud for Education Credits and explore GitHub Codespaces containers for our data science projects. Disclaimer: This blog provides instructions and resource...

During previous class, we learned how to set up a development environment with Python and Visual Studio Code (VS Code) using the UV tool. We also created a new project folder called my-first-projec...