PyData Meetup @ Bright Cape

PyData Eindhoven & Bright Cape

We’re excited to announce our upcoming meetup in collaboration with Bright Cape, focused on “Scalable AI & Cloud Deployments.” The event will be hosted in the Stage Room at Fifth NRE.

Mark your calendar and join us for an evening of insights, networking, and discussions on cutting-edge technology with fellow members of the data and AI community.

Program

• 17:30 - 18:00 Doors Open
• 18:00 - 18:10 🎤 Welcome
• 18:10 - 18:45 🎤 Lucas Bresser || Deploying AI resources using IaC with Pulumi
• 18:45 - 19:30 🍕 Food
• 19:30 - 20:00 🎤 Tom Matheussen || Building Instead of Buying: Engineering a Scalable Engine from the Inside
• 20:00 - 20:30 🎤 Dmitry Bagaev (TU/e) || Keep Calm and Trust the AI
• 21:00 🥤 Drinks and Network

==============================================================
Lucas Bresser || Sr. Data Scientist Consultant at Bright Cape
🎤 Deploying AI resources using IaC with Pulumi
AI solutions increasingly run on a mix of cloud providers, making infrastructure management complex and error‑prone. At Bright Cape, we deliberately chose Pulumi over traditional IaC tools such as Terraform and Bicep. In this session, I will show how Pulumi helps us deploy AI resources more consistently, securely, and transparently. Curious why we made this choice? I’ll walk you step by step through our reasoning and real‑world experiences.

Tom Matheussen || Product Owner Data Intelligence at Argenta
🎤 Building Instead of Buying: Engineering a Scalable Engine from the Inside
What happens when off-the-shelf AI solutions don’t fit your reality? This talk shares lessons from developing a mature in-house AI platform instead of buying one. We explore the trade-offs of build vs. buy, key engineering challenges in scalable solutions, and how engineers can play a more strategic role in shaping the product.

Dmitry Bagaev || Postdoctoral Researcher at TU/e
🎤 Keep Calm and Trust the AI
Large language models are powerful, but when asked for facts or numerical answers, they can hallucinate with surprising confidence. In this talk, we take a different approach: instead of asking an LLM to guess, we let it orchestrate real probabilistic inference. Using RxInfer and an MCP server, we connect a language interface to a Bayesian linear regression model running on an actual dataset. The LLM translates user intent into structured computation, the regression model performs principled inference, and the result is grounded in data—not generated from patterns alone. You’ll see how combining probabilistic programming with tool calling creates AI systems that are transparent, verifiable, and dramatically more reliable. Sometimes, the best way to trust the AI is to make sure it does the math.