Cheuk Ting Ho; Workshop
These may be the most important skills to keep your job or to find a new one in the future!
AI-generated code is a powerful tool, but it should never be blindly trusted. This intensive workshop, "Audit and evaluate AI generated Python code," teaches you the critical skills you need to stay relevant in the future.
Mai Gimenez; Keynote
In the era of agentic AI, our role as developers is no longer just about code creation but it’s about code curation. The blank page is dead, but AI-slop is rising. Discover how to use architectural guardrails to force AI to code with your taste, not its training data.
Brian Oginga; Talk
Learn how to build scalable multi-tenant SaaS applications with Django. This talk explains tenant isolation, schema-based architectures, and authentication strategies, with practical lessons from building real Django project
Natalia Riveros Núñez; Talk
Python developers are building APIs that expose industrial control systems (PLCs and sensor networks via Modbus or OPC-UA) to the internet, often with basic authentication and outdated dependencies. When code controls power grids, water systems, or manufacturing processes, a compromised API or a typosquatted package is not just a data leak, it can disrupt operations.
Because of this, the way these APIs are built introduces security concerns that are not always visible during development. Many of these applications depend on third-party packages, which creates exposure to supply chain risks.
Attackers use techniques such as typosquatting, publishing malicious packages with names similar to widely used libraries (such as reqeusts instead of requests). Once installed, these packages can access environment variables, API keys, or system interfaces. Outdated dependencies introduce additional risk, as libraries such as requests or urllib3 may contain known weaknesses, including credential leakage during redirects or improper SSL validation.
These patterns come from web development, where their impact is often limited to data exposure. When the same code is used in systems connected to infrastructure that controls physical processes, the consequences change. Weak API design or a compromised dependency can affect system behavior, not just stored data.
This difference creates a gap between how Python applications are commonly built and what is expected in these environments. Small decisions in Python API design can reduce risk in systems where software directly affects physical processes.
This talk shows Python developers how to secure these integrations through safer API design, careful handling of dependencies, and decisions that improve system resilience without requiring deep security expertise.
Apoorv Garg; Workshop
Most Python MCP demos work… until they hit production. In this workshop, we’ll build one live, make it secure, and avoid the common mistakes that quietly break things later.
Gertrude Abagale; Talk
The global tech ecosystem continues to grow, yet challenges like limited mentorship, a lack of role models, and fragmented community support hinder progress, especially for underrepresented groups. PyLadies offers a powerful model for bridging these gaps. This talk explores how PyLadies chapters worldwide foster technical growth, increase mentorship opportunities, and drive collaboration to create a more inclusive and sustainable global tech community.
We can contribute to the growth of the global tech ecosystem by leveraging the PyLadies model of an inclusive and sustainable community.
Brain Aboze; Talk
A snapped fridge handle. A missing washing-machine knob. A bracket that broke off your scooter. The replacement part costs three pounds and ships in two weeks, or you throw the whole thing away.
This talk is about teaching Python to fix it instead.
This talk walks through an open-source Python pipeline that takes a photo of a broken object and produces a 3D-printable patch automatically. Seven specialised agents handle calibration, segmentation, measurement, thickness estimation, mesh generation, validation, and printing. Each agent runs multiple competing computer-vision strategies, then uses an LLM to choose the most trustworthy result through a consensus protocol. The validated mesh streams straight to a BambuLab printer over MQTT, and a finished patch comes off the bed by the time the talk ends.
The whole system is built on Python, using frameworks such as FastAPI, OpenCV, Shapely, trimesh, SAM, Depth Anything, and a Three.js front end, plus an LLM acting as the decision engine inside every agent. You'll see how far Python's ecosystem reaches when the output has to obey physics, and where the interesting failures live.
Laís Carvalho; Keynote
Most of us now talk to our computers like we did in the 60s, through a terminal. The difference is what is on the other side. Along the way, we stopped writing code by hand.
This talk is about what that costs and what it buys. Treating AI systems with awe hides what they can't do and hands us back plausible slop. Named precisely, as a tool that drafts, reviews, searches, or automates a specific chore, it becomes something that can be tested, questioned, measured, and optimised.
Mariatta; Talk
In 2025, PyLadiesCon team said no to spreadsheets and instead started building our conference infrastructure: an open source web app for managing various aspects of our conference. Learn more our challenges in managing the online PyLadiesCon conference, and how we're solving our problems with Python and Django.
Julio; Talk
Stop asyncio race conditions from corrupting your AI agents. Learn the Lane Queue pattern to build deterministic, reliable autonomous systems in Python.
Juan Castillo del Río; Talk
Forecasting is one of the most important and frequent problems in machine learning.
- How many customers will visit a store next Friday?
- How many products will a factory sell three months from now?
- How many tourists will visit a city next summer?
In this talk, you will learn how to solve these types of problems with machine learning in Python.
The talk will start with a brief introduction to the necessary machine learning concepts, then explain how to transform a time series problem into a machine learning problem. It will focus on concepts that are not only essential for this approach, but for time series modelling in general.
Stefanie Molin; Workshop
Come learn how to use the Python standard library’s ast module to parse and analyze code. Using just the standard library, we will implement a couple of code analysis checks from scratch, giving you the skills and confidence to use ASTs in your own projects.
David Asem; Workshop
Managing a database and keeping application data in sync can become complex as Python applications grow. In this hands-on workshop, you’ll learn how to use SQLAlchemy ORM to structure database interactions and build clean, maintainable Python APIs. We will use a commodity pricing API as a practical example to demonstrate how real-world systems are designed.
We’ll cover how SQLAlchemy’s Object Relational Mapper (ORM) replaces scattered raw SQL with structured Python models, making database operations more consistent and easier to manage. Participants will learn how to define ORM classes, model relationships, and work with efficient query patterns.
We will also explore SQLAlchemy’s asyncio extension to show how modern Python applications interact with databases asynchronously.
Participants will leave with practical experience building a working API and a clear understanding of how to structure database-backed Python applications using SQLAlchemy ORM.
Anthonette Adanyin; Talk
Large language models dominate the AI conversation, but many real-world applications don’t need massive infrastructure or external APIs. This talk shows how Python developers can run Small Language Models (SLMs) locally using tools like Hugging Face Transformers, bitsandbytes, and lightweight RAG pipelines to build privacy-first AI systems that are faster, cheaper, and easier to deploy.
Pier Paolo La Pastina; Talk
Have you ever looked at a PySpark pipeline and assumed it would run top-to-bottom? You’d be surprised…it doesn’t. In reality, Spark builds and optimizes execution plans, and this mismatch can lead to subtle, production-grade bugs.
In this talk, I’ll walk through a real-world example where reading and updating the same tables produced inconsistent results in production. We’ll see why this happens, what Spark actually executes under the hood, and why code that looks correct can behave unexpectedly.
Join this session and you’ll learn practical design principles to make pipelines predictable: introducing explicit materializations, using declarative patterns, or imposing physical separation between sources and targets.
Hilal Işık, Anastasia Mikheeva; Workshop
AI doesn’t just reflect society—it amplifies its contradictions. From viral “bikini” image manipulation trends(!) in generative tools to hiring systems that systematically disadvantage women* in tech, we see the same pattern: erasure in one place, hypervisibility in another. In this hands-on workshop, we build AI agents with LangChain and LangGraph to detect and surface gender bias in real text systems—turning critique into code, and visibility into a technical feature.
Yulia Markelova; Talk
In Python, objects live as long as something is holding on to them—and sometimes that’s way longer than you expect. This talk explores how references, the garbage collector, and weak references interact to determine object lifetimes, with real-world examples like lru_cache quietly keeping objects alive.