MLOps Community Amsterdam Meetup @ ABN AMRO
- Date
- 2024-05-28
- Location
- Amsterdam, Noord-Holland, Netherlands
- Host
- Amsterdam MLOps Community
About this event
If you work with machine learning systems in the real world, you already know the hard part is rarely the model alone. The interesting work happens where experimentation meets production: deployment, monitoring, collaboration, governance, and the day-to-day decisions that make AI reliable inside an organization. This meetup brings the MLOps Community to Amsterdam for an in-person evening focused on exactly those conversations. About the Event MLOps Community Amsterdam Meetup @ ABN AMRO is an in-person gathering for people building, operating, and improving machine learning and AI systems. It is designed for practitioners and teams who care about what it takes to move beyond demos and turn ML into something robust, useful, and maintainable. This is a meetup, which means the format is intentionally accessible and community-driven. Expect a room with a mix of engineers, data scientists, platform teams, technical leaders, and curious builders who want to compare notes on how ML work actually gets done inside modern organizations. Because the event is hosted in person in Amsterdam, the value is not just in the content on stage. It is also in the side conversations before the talks begin, the questions that come up during discussion, and the connections you make with people facing similar technical and organizational challenges. Whether you are deep in model serving and infrastructure, thinking about evaluation and observability, or trying to improve the handoff between research and production, this meetup gives you a place to hear how others approach the same problems. What to Expect The evening begins at 6:00 PM GMT+2 on Tuesday, May 28 and is built around the kind of format that makes meetups useful: focused content, practical discussion, and room to connect. While the exact session lineup may vary, you should expect a structure that supports both learning and conversation rather than a passive lecture-only experience. A typical MLOps community meetup often includes a few core elements: Welcome and introductions to set the context for the evening Short talks or presentations related to ML systems, tooling, workflows, or organizational lessons Audience Q&A or discussion where practical tradeoffs and real-world constraints can be explored Networking time to meet others working in AI, data, and platform roles The emphasis is likely to be on applied, production-minded topics rather than abstract theory. That makes this event especially relevant if your work involves shipping models, managing ML pipelines, supporting internal AI platforms, or helping teams move from experimentation into stable operations. You can also expect a community atmosphere. Some attendees may come to learn from speakers, others to meet peers, and others to sanity-check their own approach against what other teams are doing. All of those are good reasons to be there. Why Attend If you have ever wanted more honest conversations about ML in practice, this meetup is built for that. The most useful insights in MLOps often come from hearing what worked, what broke, what was harder than expected, and what teams changed once systems met real users, real data, and real constraints. Attending gives you a chance to sharpen your thinking on questions like these: How are teams structuring ML workflows so they are repeatable and easier to maintain? What does good collaboration look like across data science, engineering, and platform functions? Where do bottlenecks usually appear when taking models into production? How are organizations thinking about reliability, monitoring, and long-term ownership for AI systems? There is also strong value in simply being in the room with the right people. Meetups like this can help you find peers who understand the technical and organizational complexity of ML work, whether you are solving for model deployment, internal tooling, experimentation speed, governance, or team process. You do not need to arrive with all the answers. In fact, this is most useful when you come with a few real questions from your current work. The event is a chance to learn something concrete, test your assumptions, and leave with a clearer sense of how others are approaching similar challenges. Practical Details This is an in-person event in Amsterdam, Netherlands, hosted at ABN AMRO. If you are based in Amsterdam or nearby, this is a good opportunity to step out from behind Slack threads and dashboards and have face-to-face conversations with people working on ML and AI in practice. The meetup starts at 6:00 PM GMT+2 on Tuesday, May 28. As with most evening community events, it is smart to arrive a little early if you want time to settle in, meet a few people before the program begins, and make the most of the networking side of the evening. Because this is a community meetup, a good way to prepare is simple: Come ready to introduce yourself and what you work on Bring a current MLOps or AI systems challenge you are thinking through Be open to both technical discussion and broader workflow or team conversations Plan to stay long enough to talk with people, not just listen and leave If your work sits anywhere between machine learning experimentation and production operations, this event is a strong fit. It is local, focused, and built around the kind of practical exchange that helps people make better decisions in real ML environments.
Who should attend
This is for people who want practical, grounded conversations about how ML and AI systems are actually built, shipped, and maintained. - You work in **machine learning engineering, data science, data engineering, software engineering, or platform engineering** and want to learn how other teams handle production ML. - You are responsible for **deploying, monitoring, maintaining, or scaling models** and want ideas you can apply to your own workflows. - You are part of a team trying to improve the handoff between **experimentation and production**, and you want to hear how others manage that transition. - You care about the broader systems around AI, including **pipelines, tooling, infrastructure, collaboration, reliability, and governance**. - You lead or support ML initiatives inside a company and want a better sense of the **organizational patterns and technical tradeoffs** that shape successful MLOps work. - You are based in or near Amsterdam and want to meet **local practitioners** who speak the same language of models, tooling, deployment, and real-world constraints. If you are looking for polished hype, this may not be your event. If you want useful conversations with people doing the work, you will likely feel at home here.