Discover the Talks at PyCon Colombia 2026 ✨
Browse every accepted session—titles, tracks, levels, and speakers—before you plan your days in Medellín.
Building AI Agents to Play Catan
Designing AI for environments with chance and imperfect information is a fascinating challenge. In this workshop, we will develop a Python agent capable of playing Settlers of Catan and compare different approaches live. Beyond the board, attendees will discover how the principles used here apply to any LLM-based agent system. You will learn to equip your agents with tools, connect services via MCP (Model Context Protocol), and structure robust architectures to optimize complex decision-making.
Machine Learning Applied to Genetic Sequences
DNA contains massive amounts of biological information, but how can artificial intelligence help us understand it? In this talk, we will explore how Python and Machine Learning can be used to analyze genetic sequences in a practical and beginner-friendly way. Using public biological datasets, we will demonstrate how DNA sequences can be transformed into data suitable for machine learning models, covering concepts such as feature extraction, sequence representation, and basic classification techniques. We will also review popular Python tools used in bioinformatics, including Biopython, pandas, and scikit-learn, while discussing real-world challenges when working with biological data, such as high dimensionality, noise, and interpretability limitations. By the end of the talk, attendees will have a clear understanding of how to start building genetic analysis projects using accessible tools from the Python ecosystem, even without prior bioinformatics experience.
Vulnerable AI Systems: Real Data, Responsible Design
29% of attacks bypass the security filters of the most widely used LLMs in production. It's not a bug. It's the nature of the system. LLMs are stochastic processes trained on human language—the most flexible, ambiguous, and manipulable medium that exists. That makes them incredibly powerful. And that's exactly why they're vulnerable. There's no patch for that. Only design. This talk presents the results of llm-break-bench: 3,360 adversarial tests on GPT-4o, Claude, Gemini, Grok, and DeepSeek using MLCommons AI Safety v0.5 and OWASP LLM Top 10 as standards. The numbers break intuitions. The smartest model in the benchmark is 5 times more vulnerable than the cheapest and 11 times more expensive. The most criticized by the press ends up second in security, and the reason behind that explains everything that's wrong with how the industry deploys AI today. The data is the starting point. The talk connects them to real use cases where LLMs are in production: RAGs, chatbots, agents, code assistants. It shows where design fails, what consequences it has (Air Canada paid for it), and how to build differently. The closing is actionable: 5 design pillars for AI systems that don't depend on the model for their own security, with real code from NVIDIA NeMo Guardrails and Meta LlamaFirewall. If you have an LLM in production or are about to, this talk changes how you design it.
How to Find Pearls on the Bottom of the Sea - Autoencoders as Anomaly Detection Model
In the AI/Machine Learning world, we often think of anomalies as errors that need to be fixed. But what if some of those anomalies are actually opportunities of immense value? Detecting these opportunities, these "pearls," is a huge challenge due to the vastness and complexity of the data ocean. There is a solution: anomaly detection models—positive ones in this case—and we will explore them in this session.
The GenAI Revolution Reaches RecSys
When we talk about the generative AI revolution, the conversation usually stays close to chatbots, image generation, and code assistants. But the same architectures that powered that wave (transformers, autoregressive modeling, scaling laws) are quietly reshaping fields most people don't associate with GenAI at all. Recommender systems are one of the most interesting examples. Meta, Netflix, Google, Spotify and others are replacing decades-old recsys pipelines with transformer-based foundation models, and the results are hard to ignore. This talk is a practical tour of that shift from a Python engineer's seat.
Structured Learning: An AI-powered platform that transforms academic papers into interactive learning experiences.
Structured Learning: The AI Platform That Other AI Agents Build" Subtitle: A real platform that converts academic papers into interactive learning modules, built as a solo developer with an agent pipeline that takes every GitHub issue to a merged PR, on pure Python, async FastAPI, and AWS What do you do when you need to understand and implement a research paper, and existing tools force you to jump between five tabs — PDF viewer, ChatGPT, IDE, notes, a search engine — and every jump breaks context? And what happens when, on the other side of the problem, you're a solo developer trying to build something serious to solve it, with AI agents that usually work well in demos but fall apart in production? This talk is the engineering story of Structured Learning: a platform that converts a research paper into a complete learning module — chapter-by-chapter explanations, incremental executable code, RAG chat, FSRS spaced-repetition flashcards, equation derivations, and a Neo4j knowledge graph connecting concepts across the user's entire library. Nineteen of forty-two features delivered as a solo developer. The product is the visible half. The interesting half is how it was built. The first topic is the product. Where a static tool promises "upload the PDF, get a summary," Structured Learning accompanies the three phases of working with a paper — researching it, understanding it, and applying it — across four custom AI agents in async FastAPI and LiteLLM, with multi-provider support across Anthropic, OpenAI, Google, DeepSeek, and Ollama. The second topic is engineering: an agentic development workflow pipeline that takes a GitHub issue to a merged PR — planning, implementation, tests, review, automatic patching, conflict resolution, documentation, and release. Each phase runs in an isolated git worktree with its own port range, so agents actually run in parallel without checkout contention. Commands in GitHub comments (/plan, /patch, /conflict) trigger background workflows that post phase-by-phase progress back to the same issue, creating a human-readable audit trail. Adaptive routing sends trivial classification to fast, cheap models, and heavy review or debugging to stronger models. PR creation is deferred until all quality gates are green — unit tests, end-to-end tests, and review. When review finds blockers, the pipeline automatically launches a patch workflow to fix them in place and re-runs verification. Real self-repair, no human in the loop for the common "almost right" case. The third topic is production: how this runs on AWS without breaking, and how we develop it locally without paying for cloud. The entire stack — S3 for PDF uploads and TTS audio cache, ECS Fargate for FastAPI backend and GROBID service, RDS PostgreSQL 16 with pgvector extension consolidating application DB and vectors, Secrets Manager for credentials, ECR with immutable image promotion — lives on Terraform with per-environment directory separation. The key to maintaining dev↔prod parity is LocalStack plus a single variable (S3_ENDPOINT_URL): the same boto3 client runs identically on both sides, no code branches, no mocks in tests. docker compose up reproduces the full AWS topology locally, enabling end-to-end flows — paper upload, audio generation, caching — without real credentials or cloud costs during development. You'll leave with a clear view of the product — a platform that covers the three phases of working with an academic paper. For research, it offers conversational search over arXiv and OpenAlex, and a Neo4j graph that detects shared concepts, knowledge gaps, and optimal reading order across your entire library. For explanation, it generates chapter breakdowns with key concepts and diagrams, step-by-step equation derivations that demystify the math, RAG chat and Socratic mode that answers with the paper's full context, and an annotatable PDF reader with "Ask AI" on any selection. And for practical application, it produces executable code that builds incrementally with dependency tracking, FSRS flashcards for measurable long-term retention, and comprehension quizzes that validate understanding chapter by chapter. And with three concrete engineering recipes to reproduce in your own stack. One for building serious AI products without magic frameworks: typed contracts with Pydantic, SSE streaming with cancellation, prompt caching, and per-task cost accounting. Another for scaling a solo developer to team velocity by applying agentic discipline to your own development cycle: isolated worktrees, resumable pipelines, auto-patching after failed review, GitHub as the agents' API. And a third for eliminating "works on my machine" from AWS infrastructure using LocalStack as a local S3 mirror. The thesis: agents don't replace engineers, they replace the glue between engineers and the boring 80% of the SDLC — and that's where compound returns live.
Feeding the Invisible: Food Security in Intermediate Cities with Python
In many countries, food insecurity is not only a social problem but also a data problem. In Colombia, key monitoring systems have lost continuity, leaving critical information gaps for public decision-making. This talk presents the development of a Python prototype to build a monitoring and prediction system for food insecurity risk in intermediate cities, using only open data. From a reproducible pipeline, multiple data science components are integrated: ingestion and processing of food price data (SIPSA), time series models for price forecasting (including classical approaches and machine learning like XGBoost), household segmentation through clustering from socioeconomic surveys, construction of a composite index relating income, prices, and vulnerability, and development of a decision support system (DSS) prototype. Attendees will take away a replicable approach for building complex indicators, strategies for working with imperfect open data, ideas for integrating models, socioeconomic data, and visualization in a single system, and a real example of applying Python in public policy and territorial development.
From ETL to Agentic Workflows: The Evolution of Data Engineering in the Generative AI Era
This hands-on workshop will explore how data engineering is evolving from traditional ETL-based pipelines toward intelligent agent-driven systems capable of reasoning, planning, and executing tasks autonomously. Through hands-on exercises, participants will learn the fundamental concepts behind agentic workflows, the new architectural patterns emerging in the industry, and the modern Python libraries that enable building these types of solutions. Tools for agent creation, task orchestration, integration with language models, and automation of complex processes will be covered. Upon completion, attendees will have built functional examples and will understand how to apply these new capabilities to transform traditional data processes into more dynamic, autonomous, and scalable systems.
From S3 to AI Agent: Your First Queryable Lakehouse
AI agents are only as good as the data they can query. The problem is that most agents built today are connected to outdated CSVs, unstructured databases, or simply nothing. What if your agent could query a real lakehouse — with versioning, schema evolution, and time travel — using natural language? In this workshop we will build exactly that, from scratch, using only open source tools that run on your laptop. What we will build together: Starting from a fully local stack based on Docker Compose, we will set up a functional lakehouse architecture using MinIO as S3-compatible storage, Apache Iceberg as the table format, Project Nessie as a Git-like versioned catalog, and Trino as the SQL query engine. On top of that, we will build an MCP server in Python that exposes our Iceberg tables as tools queryable by an AI agent — and we will finish by connecting Claude so it can query our lakehouse in natural language. What you will learn: How a modern lakehouse really works under the hood — without managed services hiding the magic How Apache Iceberg enables schema evolution, time travel, and row-level deletes on object storage How to build an MCP server in Python that turns SQL queries into tools for AI agents Why this open source architecture mirrors exactly what companies like Netflix, Airbnb, and modern data teams use in production Why open source? We deliberately replace AWS S3 with MinIO, AWS Athena with Trino, and AWS Glue with Project Nessie — not because AWS is bad, but because understanding the real components makes you a better engineer, and because this workshop should be accessible to everyone, regardless of whether you have an AWS account or not. At the end of the workshop you will have a functional lakehouse running on your machine, a working MCP server, and an architecture you can take directly to your next project.
NLP Without Labels: How to Cluster N Legal Processes of the Colombian State and Turn Chaos into a Production Classifier
What do you do when you have 600,000 legal complaints, zero labeled data, and a government entity waiting for results? This talk walks through the full process of building an unsupervised NLP classification system for the Procuraduría General de la Nación. Starting from raw administrative text—noisy, full of abbreviations and institutional jargon—I'll show how TF-IDF, truncated SVD, and KMeans combined to organize more than half a million records into 64 semantically coherent groups, without a single manual label. But clustering is only the starting point. I'll cover how clusters were validated, how a Logistic Regression classifier was trained on them to make the system deployable, and how the final pipeline was packaged in a .pkl that non-technical colleagues use in production today. Along the way we'll face real problems: elbow curves that don't behave, 1:20 size imbalances between clusters, and the tension between mathematical elegance and institutional usability. Because in the public sector, a model nobody uses isn't a model—it's a PDF gathering dust.
How We Stopped Answering Data Questions and Built the Stack That Answers Them
If you've worked at a growing startup, you probably know the feeling: multiple teams pulling different numbers for the same metric, ops constantly asking engineering for basic answers, and creating or organizing metrics that's a real pain. Every new question feels like starting from scratch. This talk is the story of how a small team fixed that. First, by building a proper dbt architecture from scratch with Sources, Staging, Intermediate, and Marts so that things like bookings, revenue, and providers were defined in one place and everyone was looking at the same number. Once the data was reliable, we connected an LLM so non-technical teammates could ask questions in plain English and get real answers directly from Snowflake. No SQL, no ticket, no waiting on engineering. It's an honest look at what we built, what broke, and what we got wrong the first time. You'll walk away with a clear mental model for building a dbt layer people actually trust, a practical architecture for connecting an LLM to your warehouse, and the one thing that made it all click: your dbt docs are your LLM prompt.
NLP in Practice: From Corpus Linguistics to RAG with Python
Natural language processing today offers a mature set of tools for analyzing textual corpora systematically and reproducibly, but the path between having the documents and obtaining results is not always clear. This workshop covers that path from start to finish. In two hours, participants will build an understanding of the NLP ecosystem: its history, logic, and methods. The session opens with a timeline from the earliest rule-based models to transformers, followed by a map of techniques organized by problem type (classification, entity extraction, semantic search, generation) so each participant can identify which method they need for a specific textual problem. The second part covers two implementations with Python. First, topic modeling with BERTopic, reviewing the internal pipeline of embeddings, UMAP, and HDBSCAN. Second, a conversational assistant with RAG: corpus indexing, semantic retrieval, and connection with a language model to answer queries about the documents. Upon completion, each participant will have a functional notebook with both pipelines and a clear map of the ecosystem to guide their own textual analysis projects.
Andrés Felipe Puerta Velez
Asistente de investigación y estudiante de maestría en matemáticas aplicadas. @ Universidad EAFIT
Biviana Marcela Suárez Sierra
Profesora vinculada al área de Computación y analística @ Universidad EAFIT
Dora Cecilia Alzate Gallo
Estudiante de la Maestría en Estudios Humanísticos @ EAFIT
Karen Melissa Gomez Montoya
Ingeniera matemática - Asistente en investigación @ Universidad EAFIT
PDF Data Extraction at Scale: When to Trust an LLM
LLMs read chaotic documents like no one else, but when money or legal liability is on the line, the question is not "can it?" but "when should I trust it, and how do I catch it when it is wrong?". In this hands-on workshop we build, step by step, a real extraction pipeline for legal PDFs with variable formats, orchestrated with Airflow, using a hybrid extractor (deterministic + LLM) and deterministic guardrails in Python. You will leave with a framework for deciding which tool to use and a reliable pattern for production environments.
PyBlend: Towards an AI Food Scientist for Nutritional Product Design
Imagine having a “food scientist” built in Python who, instead of wearing a lab coat, uses DAGs, embeddings, and LLMs to help you design nutritious powder blends. In this talk I’ll present PyBlend, an AI agent that takes a nutritional brief in natural language (for example: “I want a vegan, high‑protein, low‑sugar blend that’s suitable for dehydration”) and turns it into a quantitative formulation ready for the lab: ingredients, raw and dehydrated proportions, nutritional profile, and estimated cost. We’ll walk step by step through how to combine intelligent ingredient search (starting from one‑hot encodings and tabular features, all the way to text and nutrition embeddings), hybrid retrieval over food databases, and LLM agents orchestrated in a directed acyclic graph. Everything is implemented in Python, built on open-source libraries, and designed to be reproducible and extensible for anyone who wants to push language models beyond the classic “chatbot” use case. If you’re interested in building Python agents that do real scientific/applied work, not just answer questions, if you work with tabular data, search, optimization, or simply want to see how an LLM can end up designing a functional food formulation, this talk is for you. You’ll leave with concrete ideas, architecture patterns, and code examples you can adapt to your own domains.
Your AI Eval Is Lying To You
When you set temperature=0 and run your AI eval, you expect the same input to give the same output. It doesn't. Recent measurements on Qwen3-235B at temperature=0 produced 80 unique completions on a single prompt. So when your eval reports "92% pass rate," what does that actually mean? Is it 92% capable, 92% reliable, or 92% lucky on a small sample? This talk is about the gap between how the AI eval ecosystem talks about scores and what those scores can actually support. We walk through five specific tools that fix the gap, all anchored to published methods: 1. Pass@k versus pass^k: capability versus reliability, two different questions that one number obscures (Chen et al. 2021, OpenAI Codex paper). 2. Wilson confidence intervals with proper boundary handling, so your "92%" comes with an honest range (Brown et al. 2001). 3. Bayesian pass@k with Beta-Binomial conjugacy, when you want a posterior rather than a point estimate (Hariri et al., ICLR 2026). 4. Sequential drift detection with EWMA, CUSUM, and OLS, to catch eval regression while it's small instead of after a customer reports it (Lucas-Saccucci 1990, Page 1954, Montgomery 2012). 5. Family-wise error control via Benjamini-Hochberg, Benjamini-Yekutieli, and e-BH FDR procedures, for when you're running multiple correlated drift checks in parallel and don't want false alarms (Benjamini-Hochberg 1995, Wang-Ramdas 2022). Each method gets a short demo in pure Python with no framework dependency. The audience leaves with reference implementations they can paste into an existing pytest setup tonight. The talk also previews an open-core pytest plugin shipping in July 2026 that packages these methods into a single eval pipeline with SARIF reporting and a baseline-regression workflow. The talk shows the open primitives and the methodology that drives them. The production AI-eval ecosystem (LangSmith, Arize Phoenix, Evidently, DeepEval, Promptfoo, and others) mostly uses absolute thresholds and simple averages. None of the ten platforms I surveyed combine sequential testing with FDR control on bounded scoring scales. The framing here isn't competitive; it's a methodological gap every team shipping production AI evals will hit eventually.
Dashboards That Think: Build Agentic Analytics with Sigma
Traditional dashboards only show data: you ask, they answer, and you start over. In this hands-on workshop we will take the next leap: we will build an agentic dashboard in Sigma that reasons about data, applies business logic, and executes actions, without writing code. Working on live data in the warehouse, you will learn step by step to: design a dashboard on Sigma's canvas; incorporate Sigma Agents to answer questions in natural language, chain multiple reasoning steps, and trigger workflows; and connect the dashboard with external tools and systems via MCP, all governed and without taking data out of your platform. In the end you will have an intelligent dashboard working end to end that you can replicate in your own work. It is an ideal session to understand, hands on keyboard, what "agentic analytics" really means in today's AI conversations, and how anyone can build it without being a data engineer or analyst
