Agenda

18h30 Open Doors

19h00 Welcome & Intro

19h15 Tim Voßmerbäumer (Data & AI Consultant at Scalefree): Talk to Your Data: A Semantic-Layer Architecture for Reliable Analytics Agents

An LLM can produce valid SQL over a warehouse schema and still answer the wrong question, because the schema does not carry the organizational meaning of a metric. This talk presents an agentic system over Google Ads, Google Analytics 4, and Google Search Console data, modeled in Data Vault 2 on BigQuery, in which a governed semantic layer serves as the agent’s knowledge interface. The agent answers along two paths: questions that map to a predefined metric go through the dbt Semantic Layer, using definitions that already exist rather than ones the model writes. Questions no predefined metric covers take an exploratory path, where the agent writes its own SQL under a rule-based guard. The agent may also decline when the data cannot support an answer. Without that option it does not fail visibly, but produces SQL that passes every structural check, fabricates the rows, and reports them faithfully.

19h50 Ivan Herreros (Senior AI Consultant | Machine Learning Engineer at inovex): From ChatGPT to Agentic AI: The Common Thread of AI Development

How is Agentic AI actually progressing, how will it keep reshaping day-to-day work, and is any of it really impossible to predict? To answer that, we trace the evolution of LLM-based applications: from natural-language dialogue to the delegation of digital workflows to agents that act autonomously over ever-longer time horizons. The common thread: viewed with some distance, most of the steps (from RAG through MCP to Claude Code and Agent Harnesses) were not just predictable but predicted. Drawing on almost three years of building conversational and agentic AI platforms, we show which patterns recur and what actually sits behind the buzzwords. Whoever understands this underlying logic can situate new technological developments proactively, instead of reacting to every new hype cycle.

20h20 Networking with food and drinks provided by KölnBusiness

Join the discussion on CAIML #44 here.

We will have two talks with additional time for networking.

Talk 1: Tim Voßmerbäumer (Data & AI Consultant at Scalefree): Talk to Your Data: A Semantic-Layer Architecture for Reliable Analytics Agents

Talk 2: Ivan Herreros (Senior AI Consultant | Machine Learning Engineer at inovex): From ChatGPT to Agentic AI: The Common Thread of AI Development

Follow us on LinkedIn to learn more about the talks and the agenda, and click here to secure your spot. See you in September! 🤖

Agenda

18h30 Open Doors

19h00 Welcome & Intro

19h15 Gabriel Firmino Barjollo & Sebastian Kröger (Cybersecurity Specialist, Pentester & AI Engineer; Architect & AI Engineer): Background of jailbreaking LLMs, Pentest Agents, and Supply Chain Attacks

Modern LLM safety alignment is a moving target - and understanding where it breaks is essential to building better defenses. We introduce in the background of jailbreak techniques across prompt, system, and model levels, then demonstrate how an unaligned model becomes the reasoning core of an autonomous pentest agent, wired to standard offensive tooling with a plan-execute-observe-adapt loop. Once an AI agent acts on a developer’s behalf, how far can a compromise propagate? We walk through a realistic end-to-end supply chain attack - a manipulated agent introduces a malicious dependency that survives code review and flows through CI/CD to downstream consumers. Attack primitives include LLM-amplified typosquatting, prompt injection via package metadata, and lateral movement into build and signing infrastructure.

19h50 Maurice Kraus (Research Associate / PhD Candidate at Technische Universität Darmstadt): Do We Really Need Another Forecasting Model?

Time-series foundation models promise a single solution across domains, yet often rely on architectures that may be ill-suited to latency-sensitive or specialized use cases. In contrast, xLSTM-Mixer illustrates how lighter recurrent and state-space-inspired architectures can compete with heavier transformer models in both accuracy and efficiency. Benchmarks such as QuAnTS further highlight that practical time-series modeling increasingly goes beyond forecasting, requiring capabilities such as question answering and reasoning over temporal data, often through more specialized models. Finally, recent work on finding the Zeitgeist in time-series foundation models shifts the question from prediction error alone to what these models actually learn, and how their representations transfer. This talk introduces xLSTM-Mixer and uses it to motivate a broader discussion: do we need another forecasting method, or better ways to design, understand, and evaluate models for the specific demands of each domain?

20h20 Networking with food and drinks provided by IQMatix

We will have two talks with additional time for networking.

Talk 1: Gabriel Firmino Barjollo (Cybersecurity Specialist, Pentester & AI Engineer) & Sebastian Kröger (Architect & AI Engineer): Background of jailbreaking LLMs, Pentest Agents, and Supply Chain Attacks

Talk 2: Maurice Kraus (Research Associate / PhD Candidate at Technische Universität Darmstadt): Do We Really Need Another Forecasting Model?

Follow us on LinkedIn to learn more about the talks and the agenda, and click here to secure your spot. See you in July! 🤖

Agenda

18h30 Open Doors

19h00 Welcome & Intro

19h15 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.

19h50 Simonas Cerniauskas (Founder 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.

20h20 Networking with food and drinks provided by MobiLab Solutions

Join the discussion on CAIML #42 here.

We will have two talks with additional time for networking.

Talk 1: Witold Czaplewski (Full Stack Engineer at MobiLab Solutions): AI Engineering on Databricks

Talk 2: Simonas Cerniauskas (Machine Learning Engineer at tisix.io): Building Reliable AI Agents: A DSPy-based Quality Assurance Framework

Follow us on LinkedIn to learn more about the talks and the agenda, and click here to secure your spot. See you in May! 🤖

Agenda

18h30 Open Doors

19h00 Welcome & Intro

19h15 Moritz Wegener (Data Scientist at ATVANTAGE): Nodes, Networks, and Capital Myths: How Graphs Uncover Dubious Payment Networks

This talk explains how heterogeneous entities can be consolidated into meaningful graph-based units—such as individuals, companies, and corporate groups—and how these structures enable the detection of suspicious payment networks. By modeling ownership, control, and transactional relationships as graphs, we demonstrate how network patterns and structural anomalies help identify candidates for fraudulent activity that remain hidden in traditional, flat data representations.

19h50 Achim Reiz & Uwe Kloss: Lightning Talk

A big thank you to Achim and Uwe for jumping in on short notice!

20h20 Networking with food and drinks provided by ATVANTAGE

Join the discussion on CAIML #41 here.

We will have two talks with additional time for networking.

Talk 1: Moritz Wegener (Data Scientist at ATVANTAGE): Nodes, Networks, and Capital Myths: How Graphs Uncover Dubious Payment Networks

Talk 2: Gergő Szita (Data Scientist): ICG-enhanced FOI of Rheumatoid Arthritis Classification

Follow us on LinkedIn to learn more about the talks and the agenda, and click here to secure your spot. See you in March! 🤖

Agenda

18h30 Open Doors

19h00 Welcome & Intro

19h15 Billy Kunze (AI Agent Engineer at Nandu) & Nils Zündorf (Co-Founder at Nandu): Teaching LLMs to Work with Data: From Zero to Production with LangGraph & LangSmith

LLMs are great at language — but terrible with data. Over the past year, we built a production system that bridges that gap: combining LangGraph and LangSmith with real analytics pipelines to turn raw data into contextual insights. This talk shares our hands-on learnings from starting at zero to running a full production setup in just twelve months: how we structured multi-agent workflows, managed retrieval and context, and connected the system to SQL-based sources and evaluation traces. Expect a mix of architecture lessons, pitfalls, and examples of how to make LLMs reason with data, not just talk about it.

19h50 Alexander Khachikyan (Principal AI & Data Scientist at PAYBACK) & Falko Trischler (Director Data Science & Machine Learning Engineering at PAYBACK): Intelligent Agents in Loyalty Programs: PAYBACK’S Experiences with Agentic AI

Autonomous Agents that perform tasks proactively and adapt to complex environments hold immense potential for automation and personalization. But how to bridge the gap from theory to productive reality within a complex enterprise environment? This talk offers an honest dive into PAYBACK‘s enterprise path with Agentic AI. We’ll detail what worked well, where we encountered hurdles, and the lessons learned in bringing AI agents into production across multiple domains. Through concrete use cases we’ll share first-hand insights. We’ll also demonstrate how PAYBACK leverages LLMOps and multimodal models to drive efficiency, creativity, and scalability within our loyalty programs.

20h20 Networking with food and drinks provided by PAYBACK

Join the discussion on CAIML #40 here.

We will have two talks with additional time for networking.

Talk 1: Billy Kunze (AI Agent Engineer at Nandu) & Roberto Russo (Co-Founder at Nandu): Teaching LLMs to Work with Data: From Zero to Production with LangGraph & LangSmith

Talk 2: Alexander Khachikyan (Principal AI & Data Scientist at PAYBACK) & Falko Trischler (Director Data Science & Machine Learning Engineering at PAYBACK): Intelligent Agents in Loyalty Programs: PAYBACK’S Experiences with Agentic AI

We will share more details on the talks and an agenda soon. See you in January 🤖

Click here to secure your spot.