4:30 CH - 5:30 CH (UTC)

Our fourth stream in the Python + AI series is all about vision models! Vision models are LLMs that can accept both text and images, like GPT 4o and GPT 4o-mini. You can use those models for image captioning, data extraction, question-answering, classification, and more! We'll use Python to send images to vision models, build a basic chat app with image upload, and even use vision models inside a RAG application. Follow-along live, thanks to GitHub Models and GitHub Codespaces. If you'd like to follow along with the live examples, make sure you've got a GitHub account. You can also join a weekly office hours to ask any questions that don't get answered in the chat, in our AI Discord. Check out more resources here

4:30 CH - 5:30 CH (UTC)

In our fifth stream of the Python + AI series, we're going to explore the two main ways to get LLMs to output structured responses that adhere to a schema: function calling and structured outputs. We'll start with function calling, which is the most well supported way to get structured responses, and discuss its drawbacks. Then we'll focus on the new structured outputs mode available in OpenAI models, which can be used with Pydantic models and even used in combination with function calling. Our examples will demonstrate the many ways you can use structured responses, like entity extraction, classification, and agentic workflows. Follow-along live, thanks to GitHub Models and GitHub Codespaces. If you'd like to follow along with the live examples, make sure you've got a GitHub account. You can also join a weekly office hours to ask any questions that don't get answered in the chat, in our AI Discord. Check out more resources here

4:30 CH - 5:30 CH (UTC)

In our final session of the Python + AI series, we're culminating with a crucial topic: how to use AI safely, and how to evaluate the quality of AI outputs. There are multiple mitigation layers when working with LLMs: the model itself, a safety system on top, the prompting and context, and the application user experience. Our focus will be on Azure tools that make it easier to put safe AI systems into production. We'll show how to configure the Azure AI Content Safety system when working with Azure AI models, and how to handle those errors in Python code. Then we'll use the Azure AI Evaluation SDK to evaluate the safety and quality of the output from our LLM. Follow-along live, thanks to GitHub Models and GitHub Codespaces. If you'd like to follow along with the live examples, make sure you've got a GitHub account. You can also join a weekly office hours to ask any questions that don't get answered in the chat, in our AI Discord. Check out more resources here