CAIML Meetup
Cologne AI ML
The “Cologne AI Machine Learning Meetup” (CAIML) is the leading deep tech community in the Rhineland region and regularly organises meetups at various venues across Cologne. The events feature excellent specialist talks and practical tech demonstrations, including the underlying source code. The meetup brings together academics and researchers, professional software developers and industry experts, as well as interim managers, to share expertise and network.
Venue
The 42nd CAIML meeting was held at MobiLab Solutions GmbH, which also sponsored the delicious food and drinks – many thanks for that.
Welcome & Intro
As always, the CAIML meeting was brilliantly and expertly chaired by Fabian Hadiji. The evening featured two presentations, followed by a social gathering of the AI enthusiasts in attendance.
First Talk
Witold Czaplewski (Full Stack Engineer at MobiLab Solutions): AI Engineering on Databricks
“Writing a prompt is easy. Building a production-ready GenAI system is not. In this session, we explore what AI engineering means in practice and how Databricks supports the full lifecycle of GenAI applications, from experimentation to deployment, monitoring, and governance. We cover the core concepts behind production AI systems, including the roles of software engineering, data engineering, and data science. The session walks through the end-to-end GenAI lifecycle on Databricks, including MLflow, batch and real-time inference, prompt management, and serving models or agents. Finally, we address governance, security, and responsible AI, with examples related to the EU AI Act, transparency, data governance, and operational risk management. A short live demo complements the discussion and shows how these capabilities come together in a practical setup.”
Second Talk
Dr.-Ing. Simonas Cerniauskas (Founder, CTO and Managing Director at tisix.io): Building Reliable AI Agents: A DSPy-based Quality Assurance Framework
“As publishers increasingly adopt AI agents for content generation and analysis, ensuring output quality and reliability becomes critical. This talk introduces a novel quality assurance framework built with DSPy that addresses the unique challenges of evaluating AI agents in publishing workflows. Using real-world examples from newsroom implementations, I will demonstrate how to design and implement systematic testing pipelines that verify factual accuracy, content consistency, and compliance with editorial standards. Attendees will learn practical techniques for building reliable agent evaluation systems that go beyond simple metrics to ensure AI-generated content meets professional publishing standards. This presentation addresses one of the most pressing challenges in professional publishing today: ensuring quality and reliability when deploying AI agents in editorial environments. We’ll take a deep dive into how DSPy’s programmatic approach to language model development can be leveraged to create robust testing and validation pipelines that meet the demanding standards of modern newsrooms. The discussion begins by exploring the current landscape of AI evaluation in publishing workflows, examining why traditional testing approaches fall short when dealing with language models, and identifying the specific quality requirements unique to journalistic and editorial content. We’ll then move into a detailed technical exploration of solutions built with DSPy, demonstrating how to design modular evaluation pipelines, implement publishing-specific metrics, and create automated systems for fact-checking and consistency validation. Special attention will be given to the integration of knowledge graphs for reference-based evaluation and the incorporation of these systems into broader MLOps workflows. To ground these concepts in reality, we’ll examine a detailed case study of implementing this framework in an actual newsroom environment. This will include practical discussions of handling various content types, along with strategies for managing test data and evaluation criteria. We’ll share real-world performance monitoring approaches and concrete improvement strategies that have proven successful in production environments. The presentation concludes with hard-won insights and best practices, including practical strategies for finding the right balance between automated testing and human review, effective approaches to handling edge cases, and methods for scaling quality assurance processes across diverse content teams. Throughout the talk, we’ll share code examples and practical implementations that attendees can adapt for their own projects.”
Video impressions
Witold Czaplewski
Simonas Cerniauskas
Further information
CAIML aims to bring together people interested in AI and ML (machine learning). For more information visit & join us: Cologne AI and Machine Learning Meetup
CAIML Mission
“We want to bring people together who are interested in AI and Machine Learning. At our meetups, we have: networking, talks, fireside chats, knowledge exchange and applications of AI and Machine Learning. We organize our meetups every other month at different locations in Cologne. We are always looking for innovative and inspiring speakers. If you know somebody who would be an excellent fit for our meetup, we would highly appreciate if you help us and recommend this speaker to us.”
Please follow our CAIML community also on Linkedin: www.linkedin.com/company/caiml-meetup/posts
Acknowledgement
Thanx for Aaqib, Marc & Fabian for their consistently excellent organisation of the high-quality CAIML community and for their engaging moderation of this meetup. (C)opyright for the group photo belongs to MobiLab Solutions.
Retrospective
Don’t miss the previous meetups of the CAIML community in Cologne:
Next CAIML event
Tuesday, July 21 from 6:30 to 8:30 PM in Hürth: we are looking forward to meeting you.
