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For our first meetup of 2026, we're bringing you two deeply technical stories from the front lines of applied AI, together with AI Native Netherlands. We'll hear how the ANWB navigates the challenges of imperfect data in a legacy organization, and then dive into a practical guide for building production-grade AI agentic workflows with Elastic.

We’ll cover:

• ANWB's journey from manual operations to real-world AI predictions.
• Balancing the human and technical challenges of AI innovation.
• A practical guide to building production-grade agentic workflows.
• Integrating LLMs, vector search, and security into a simplified stack.

Speakers 1: Yke Rusticus & David Brummer (ANWB) Yke is a data engineer at ANWB with a background in astronomy and artificial intelligence. In the industry, he learned that AI models and algorithms often do not get past the experimentation phase, leading him to specialise in MLOps to bridge the gap between experimentation and production. As a professional in this field, Yke has developed ML platforms and use cases across different cloud providers, and is passionate about sharing his knowledge through tutorials and trainings.

David is a self-acclaimed “not your typical Data Scientist” who loves analogue photography, vegan food, dogs, and holds an unofficial PhD in thrifting and sourcing second-hand pearls. With a background in growth hacking and experience in the digital marketing trenches of a startup, a scale-up, and a digital agency, he now brings together lean startup thinking, marketing know-how, and sales pitches, blending it all with a passion for creativity and tech at the ANWB. As a bridge between business and data, David focuses on building AI solutions that don’t just work, but actually get used.

Talk: How AI is helping you back on the road We learn at school what AI can do when the data is perfect. We learn at conferences what AI can do when the environment is perfect. In this talk, you'll learn what AI can do when neither is perfect. This story is about the process of overcoming these challenges in an organisation that has been around since the invention of the bike. We'll balance the technical aspect of these solutions with the human aspect throughout the talk. Because in the end, it's not actually AI helping you back on the road, it's people.

Speaker 2: Hans Heerooms (Elastic) Hans Heerooms is a Senior Solutions Architect at Elastic. He has worked in various roles, but always with one objective: helping organisations to get the most out of their data with the least amount of effort. His current role at Elastic is all about supporting Elastic’s customers to help them evolve from data driven decisions to AI guided workflows.

Talk: Building Production-Grade AI Agentic Workflows with Elastic This talk tells and shows how Elastic Agent Builder can help to build and implement agentic workflows. It addresses the complexity of traditional development by integrating all necessary components—LLM orchestration, vector database, tracing, and security—directly into the Elasticsearch Search AI Platform. This talk will show you how to build custom agents, declare and assign tools, and start conversations with your data.

Agenda: 17:45 — Arrival, food & drinks 18:30 — Talk #1 \| Yke & David (ANWB) 19:15 — Short break 19:30 — Talk #2 \| Hans Heerooms (Elastic) 20:15 — Open conversation, networking & more drinks 21:00 — Wrapping up

Please note that the main door will close at 18.00. You will still be able to enter our office, but we might ask you to wait a little bit while we come down to open the door for you.

What to bring: Just curiosity and questions. If you're working on MLOps, applied AI, or building agentic workflows, we’d love to hear your thoughts.

Who this is for: Data scientists, AI/ML engineers, data engineers, MLOps specialists, SREs, architects, and engineering leaders focused on building and using real-world AI solutions.

Where to find us: Elastic's office in Amsterdam Keizersgracht 281, 1016 ED Amsterdam

Hi everyone, Many of you asked for more practical, real-world AI use-cases, and we listened!

For our first meetup of 2026, we're bringing you two deeply technical stories from the front lines of applied AI. We'll hear how the ANWB navigates the challenges of imperfect data in a legacy organization, and then dive into a practical guide for building production-grade AI agentic workflows with Elastic.

A huge thank you to our friends at Elastic for hosting us at their Amsterdam office. Food and drinks will be provided!

We’ll cover:

• ANWB's journey from manual operations to real-world AI predictions.
• Balancing the human and technical challenges of AI innovation.
• A practical guide to building production-grade agentic workflows.
• Integrating LLMs, vector search, and security into a simplified stack.

Speakers 1: Yke Rusticus & David Brummer (ANWB) Yke is a data engineer at ANWB with a background in astronomy and artificial intelligence. In the industry, he learned that AI models and algorithms often do not get past the experimentation phase, leading him to specialise in MLOps to bridge the gap between experimentation and production. As a professional in this field, Yke has developed ML platforms and use cases across different cloud providers, and is passionate about sharing his knowledge through tutorials and trainings.

David is a self-acclaimed “not your typical Data Scientist” who loves analogue photography, vegan food, dogs, and holds an unofficial PhD in thrifting and sourcing second-hand pearls. With a background in growth hacking and experience in the digital marketing trenches of a startup, a scale-up, and a digital agency, he now brings together lean startup thinking, marketing know-how, and sales pitches, blending it all with a passion for creativity and tech at the ANWB. As a bridge between business and data, David focuses on building AI solutions that don’t just work, but actually get used.

Talk: How AI is helping you back on the road We learn at school what AI can do when the data is perfect. We learn at conferences what AI can do when the environment is perfect. In this talk, you'll learn what AI can do when neither is perfect. This story is about the process of overcoming these challenges in an organisation that has been around since the invention of the bike. We'll balance the technical aspect of these solutions with the human aspect throughout the talk. Because in the end, it's not actually AI helping you back on the road, it's people.

Speaker 2: Hans Heerooms (Elastic) Hans Heerooms is a Senior Solutions Architect at Elastic. He has worked in various roles, but always with one objective: helping organisations to get the most out of their data with the least amount of effort. His current role at Elastic is all about supporting Elastic’s customers to help them evolve from data driven decisions to AI guided workflows.

Talk: Building Production-Grade AI Agentic Workflows with Elastic This talk tells and shows how Elastic Agent Builder can help to build and implement agentic workflows. It addresses the complexity of traditional development by integrating all necessary components—LLM orchestration, vector database, tracing, and security—directly into the Elasticsearch Search AI Platform. This talk will show you how to build custom agents, declare and assign tools, and start conversations with your data.

Agenda: 17:45 — Arrival, food & drinks 18:30 — Talk #1 \| Yke & David (ANWB) 19:15 — Short break 19:30 — Talk #2 \| Hans Heerooms (Elastic) 20:15 — Open conversation, networking & more drinks 21:00 — Wrapping up

Please note that the main door will close at 18.00. You will still be able to enter our office, but we might ask you to wait a little bit while we come down to open the door for you.

What to bring: Just curiosity and questions. If you're working on MLOps, applied AI, or building agentic workflows, we’d love to hear your thoughts.

Who this is for: Data scientists, AI/ML engineers, data engineers, MLOps specialists, SREs, architects, and engineering leaders focused on building and using real-world AI solutions.

Where to find us: Elastic Amsterdam Keizersgracht 281, 1016 ED Amsterdam

In this episode, we talk with Daniel, an astrophysicist turned machine learning engineer and AI ambassador. Daniel shares his journey bridging astronomy and data science, how he leveraged live courses and public knowledge sharing to grow his skills, and his experiences working on cutting-edge radio astronomy projects and AI deployments. He also discusses practical advice for beginners in data and astronomy, and insights on career growth through community and continuous learning.TIMECODES00:00 Lunar eclipse story and Daniel’s astronomy career04:12 Electromagnetic spectrum and MEERKAT data explained10:39 Data analysis and positional cross-correlation challenges15:25 Physics behind radio star detection and observation limits16:35 Radio astronomy’s advantage and machine learning potential20:37 Radio astronomy progress and Daniel’s ML journey26:00 Python tools and experience with ZoomCamps31:26 Intel internship and exploring LLMs41:04 Sharing progress and course projects with orchestration tools44:49 Setting up Airflow 3.0 and building data pipelines47:39 AI startups, training resources, and NVIDIA courses50:20 Student access to education, NVIDIA experience, and beginner astronomy programs57:59 Skills, projects, and career advice for beginners59:19 Starting with data science or engineering1:00:07 Course sponsorship, data tools, and learning resourcesConnect with Daniel Linkedin -   / egbodaniel   Connect with DataTalks.Club: Join the community - https://datatalks.club/slack.htmlSubscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/...Check other upcoming events - https://lu.ma/dtc-eventsGitHub: https://github.com/DataTalksClubLinkedIn -   / datatalks-club   Twitter -   / datatalksclub   Website - https://datatalks.club/

​In this episode, we’re joined by Daniel Egbo, an astrophysicist turned ML engineer and AI ambassador (Arize, Tavily). Daniel will talk about the moment he decided to try data science and ML and what he transferred from astronomy.

​We’ll delve into how he selects resources, stays motivated during self-learning, and overcomes obstacles.

​We plan to cover:

• ​Why Daniel decided to try data science and ML and skills from astronomy that helped
• ​How he chooses learning resources in a noisy landscape
• ​What keeps him motivated and the first steps when he feels stuck
• ​Building a career from Africa/remotely
• ​Daniel’s experience with ML Zoomcamp

​ ​About the speaker

​Daniel Egbo is an astrophysicist turned machine learning engineer and AI ambassador (Arize, Tavily). A PhD candidate at the University of Cape Town, he builds end-to-end ML and LLM applications with a focus on reliability and learning in public. His work spans knowledge-retrieval assistants, practical evaluation, and applying data science to astronomy. ​

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