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Data science requires a different type of copilot than software development. But current tools are not fit for purpose. How can you use AI-assisted development to expedite your work, produce better models and more production-ready pipelines?

Discussion on AI-assisted development for data science, tooling evolution, safety, fairness, and compliance in regulated industries. Examples draw from healthcare; references to open-source libraries and standards such as LangTest, HELM, NIST, CHAI, and ISO.

Please register here: https://pipple.nl/en/pydata-x-pipple-meetup/

The power of data and AI in practice

AI goes beyond experiments and isolated analyses. During this meetup, speakers from Pipple and PyData Eindhoven will share concrete cases and technical learnings from practice, from data model to deployment. Discover how organisations are making the transition from smart analyses to solutions that are actually used: reliable, scalable and with practical impact.

Meet the community, share your knowledge

Together with PyData Eindhoven, we are bringing data scientists, engineers and AI professionals together for an evening full of content and inspiration. Expect three technical talks, practical examples and valuable conversations with colleagues who work daily on the theme of from data to production.

| 17:30-18:00 | Walk-in & welcome | | ----------- | ----------------- | | 18:00-18:15 | Intro PyData Eindhoven & Pipple | | 18:15-18:45 | From code to production: how to keep pipelines running smoothly at scale

In this talk, Yannick and Joshua will share how they designed and evolved the CI/CD pipelines that power a global retailer’s data platform across 30+ countries. From the first setup to today’s architecture, they’ll dive into what worked, what didn’t, and why. Expect real-world lessons around dbt, Terraform, and security – from failed builds to the automation wins that finally made life easier.

This session offers practical insights for anyone working with data:from engineers maintaining pipelines to analysts and ML developers relying on them. | | 18:45-19:00 | Break & pizza 🍕 | | 19:00-19:30 | Learnings from integrating AI within CyberBench: the good, the bad and the ugly

In this talk, Lucas shares his journey of building CyberBench: a framework that automatically benchmarks Large Language Models (LLMs) for direct security risks such as prompt injections and data leaks.

After a brief introduction to the cybersecurity risks of GenAI systems and the motivation behind CyberBench, Lucas dives into the technical lessons learned from integrating LLM-based components into a software system. Expect practical insights into building reliable and efficient LLM pipelines and lessons on what worked (and what didn’t).

In short: learn to maximize the good, minimize the bad, and make the ugly a little prettier when building LLM systems in production. | | 19:30-20:00 | Speaker 3 – to be announced! |

Closing remarks by Angelica Malin

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