PyCon Lithuania 2026 Schedule

Date & Time	Room	Title	Speakers	Abstract
TBA	TBA	Stats Meets ML - What I learned from my Machine Learning Certification	James Donahue	Statisticians and machine learning specialists have a lot to learn from each other (even if they don't think so). This talk lightheartedly awards points to both classical statistics and machine learning, with an attempt not to offend anyone (but to annoy everyone). Topics include: Are confidence intervals worth it? What is bias, anyway? Can I just code it in Python?
TBA	TBA	Python for Data Quality in 2025: Why tests alone are no longer enough	Artsem	In 2025, classic data tests via Python are not enough. During 25-30 minutes talk I will show how Python powers modern Data Quality: from real-time freshness checks to anomaly detection and orchestrator integration. No AI hype: starting with quick Data Quality overview and problem statement I will show practical code, architecture, and hands-on engineering for resilient pipelines from Data Engineer/Data Quality Engineer perspective.
TBA	TBA	Documenting Python Code	Christian Heitzmann	Good documentation doesn’t happen by accident. But it also doesn’t have to be painful. This talk shows how Python developers can integrate documentation naturally into their daily work. We’ll look at docstrings, turn them into readable docs with reStructuredText and Sphinx, and learn how few organizational measures, docs-as-code practices, and automation can help—or get in the way …
TBA	TBA	Measuring Experiments in LLMs: A/B Tests and Automated Testing	Kader Miyanyedi, Özge Çinko	Even small changes in LLMs can impact output quality, safety, and user experience. In this talk, we’ll show how to log experiments with Langfuse, automate tests with Pytest, and enrich them using Hypothesis-generated random data scenarios. Participants will learn how to use code, tests, and data-driven A/B tests to improve LLM development.
TBA	TBA	Creative Data Storytelling with Python	Purva	Python enables data professionals to move beyond analysis and transform information into clear, compelling stories. With various libraries, Python supports insightful exploration, expressive visualizations, and interactive elements that enhance communication. This talk highlights practical techniques for turning patterns, trends, and insights into engaging narratives, making data more understandable, impactful, and actionable.
TBA	TBA	Serializing and displaying trees	Petras Zdanavičius	We at One Codex work in the microbiology field. This means that we deal with massive taxonomy trees all the time. It is impossible to cover everything, so in this talk, I am going to focus on the best data structure to: - store tree data - serialize it - display it on the frontend
TBA	TBA	Stop Using ORM	Roman Zaiev	SQL is an excellent DSL for relational data, but ORMs hide it behind their own leaky abstractions. This talk shows how to build a cleaner persistence layer and use Postgres to its fullest, without the overhead you never asked for.

Stats Meets ML - What I learned from my Machine Learning Certification

TBA

Room: TBA

James Donahue

Python for Data Quality in 2025: Why tests alone are no longer enough

TBA

Room: TBA

Artsem

Documenting Python Code

TBA

Room: TBA

Christian Heitzmann

Measuring Experiments in LLMs: A/B Tests and Automated Testing

TBA

Room: TBA

Kader Miyanyedi, Özge Çinko

Creative Data Storytelling with Python

TBA

Room: TBA

Purva

Serializing and displaying trees

TBA

Room: TBA

Petras Zdanavičius

Stop Using ORM

TBA

Room: TBA

Roman Zaiev

7 talk(s) total.

Date & Time

Room

Title

Speakers

Abstract

TBA

Stats Meets ML - What I learned from my Machine Learning Certification

James Donahue

Statisticians and machine learning specialists have a lot to learn from each other (even if they don't think so). This talk lightheartedly awards points to both classical statistics and machine learning, with an attempt not to offend anyone (but to annoy everyone). Topics include: Are confidence intervals worth it? What is bias, anyway? Can I just code it in Python?

TBA

Python for Data Quality in 2025: Why tests alone are no longer enough

Artsem

In 2025, classic data tests via Python are not enough. During 25-30 minutes talk I will show how Python powers modern Data Quality: from real-time freshness checks to anomaly detection and orchestrator integration. No AI hype: starting with quick Data Quality overview and problem statement I will show practical code, architecture, and hands-on engineering for resilient pipelines from Data Engineer/Data Quality Engineer perspective.

TBA

Documenting Python Code

Christian Heitzmann

Good documentation doesn’t happen by accident. But it also doesn’t have to be painful. This talk shows how Python developers can integrate documentation naturally into their daily work. We’ll look at docstrings, turn them into readable docs with reStructuredText and Sphinx, and learn how few organizational measures, docs-as-code practices, and automation can help—or get in the way …

TBA

Measuring Experiments in LLMs: A/B Tests and Automated Testing

Kader Miyanyedi, Özge Çinko

Even small changes in LLMs can impact output quality, safety, and user experience. In this talk, we’ll show how to log experiments with Langfuse, automate tests with Pytest, and enrich them using Hypothesis-generated random data scenarios. Participants will learn how to use code, tests, and data-driven A/B tests to improve LLM development.

TBA

Creative Data Storytelling with Python

Purva

Python enables data professionals to move beyond analysis and transform information into clear, compelling stories. With various libraries, Python supports insightful exploration, expressive visualizations, and interactive elements that enhance communication. This talk highlights practical techniques for turning patterns, trends, and insights into engaging narratives, making data more understandable, impactful, and actionable.

TBA

Serializing and displaying trees

Petras Zdanavičius

We at One Codex work in the microbiology field. This means that we deal with massive taxonomy trees all the time. It is impossible to cover everything, so in this talk, I am going to focus on the best data structure to: - store tree data - serialize it - display it on the frontend

TBA

Stop Using ORM

Roman Zaiev

SQL is an excellent DSL for relational data, but ORMs hide it behind their own leaky abstractions. This talk shows how to build a cleaner persistence layer and use Postgres to its fullest, without the overhead you never asked for.