​**Mastering nodes, integrations, debugging, and deployment through a real-world AI content automation project — Moein Foroughi**

​In this hands-on workshop, we’ll go beyond drag-and-drop demos — exploring how **n8n** really works under the hood, how to connect it with external APIs and databases, debug complex flows, and manage production-grade deployments.

​Along the way, we’ll build a real, functional project: an **AI-assisted article writer** that integrates search, scraping, and structured data storage. This project serves as a practical example to apply every key concept of n8n — from node logic and credentials management to error handling, scaling, and containerisation.

​We’ll cover the following steps:

* ​How n8n works and how to use its core nodes effectively
* ​Connecting n8n with APIs, databases, and external tools
* ​Building and troubleshooting real workflows
* ​Setting up n8n with Docker and basic deployment tips
* ​Creating a simple AI-powered SEO article writer as a hands-on project

​By the end, you’ll know how to build and run powerful n8n workflows that connect seamlessly with modern AI tools, data systems and tools.

​**Level:**
​Beginner to Intermediate — basic familiarity with Docker, Linux, and modern AI tools will help you get the most out of the session, but all key concepts will be introduced from first principles.
​

​**About the speaker:**

​**Moein Foroughi** is a DevOps engineer focused on automation and scalable systems, with a professional interest in applying AI and modern technologies to improve engineering workflows and operational efficiency.

Berlin DataTalksClub Group

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Masood Khalid

Teryl

Gokhul Gopinathan

Juan Pablo Ruiz

Tejas Muralidhar

Alex Bogdanov

julius.lagodny

Ciprian-Flavius Hancu

Briac

Christoph Lindemann

Bernhard Sczuka

den.rybakov

Adam Vajda

Yulia Vilensky

Sergej Reznik

Konstantina Lazaridou

Heramb Nishad

Rupali Gurani

Omer

n8n: From Fundamentals to Building Intelligent Automation Pipeline

Online event

Alexey Grigorev is hosting a live hands-on workshop to explore how Docker can simplify your data workflows, from setting up databases to packaging your scripts for reproducibility.
​This session is open to everyone interested in learning practical Docker skills for data engineering and analytics.
​During the workshop, we’ll walk through a complete workflow using Docker, PostgreSQL, pgAdmin, and Docker Compose, showing how to run and connect multiple services with minimal setup effort.
​
​What you’ll learn:

* ​Why Docker is essential for modern data work
* ​Running PostgreSQL in a Docker container and connecting it to your local environment
* ​Connecting with pgcli or directly from Jupyter notebooks
* ​Loading and exploring the NYC Taxi dataset
* ​Managing databases visually with pgAdmin 4 (latest UI)
* ​Converting notebooks to scripts and Dockerizing them
* ​Running multi-container setups with Docker Compose
* ​Understanding Docker networking, port mapping, and volumes

​The workshop will be recorded and later used to refresh the Docker module of the [Data Engineering Zoomcamp](https://datatalks.club/blog/data-engineering-zoomcamp.html), so you’ll also get a preview of what’s coming in the new course release.
​
​**Thinking about DE Zoomcamp?**
​Data Engineering Zoomcamp is a free 9-week course on building production-ready data pipelines. The next cohort starts in January 2026. [Join the course here](https://airtable.com/appzbS8Pkg9PL254a/shr6oVXeQvSI5HuWD).
​
​**About the Speaker**
​Alexey Grigorev is the Founder of DataTalks.Club and creator of the Zoomcamp series.
​​Alexey is a seasoned software and ML engineer with over 10 years in engineering and 6+ years in machine learning. He has deployed large-scale ML systems at companies like OLX Group and Simplaex, authored several technical books including Machine Learning Bookcamp, and is a Kaggle Master with a 1st place finish in the NIPS'17 Criteo Challenge.

**Join our slack: https://datatalks.club/slack.html**
​

keskinelif691

Nisha Gowda

Andrey Kuchko

Idrissi Radi Ahmed

jordicarbonellsilva

kalyanikumar

NikolceK

Vasile Gheorghe

Aoun Raza

Hanokh Aloni

Maiia

Eugene

Ziye Li

Guillem Puig Comerma

Sławomir Duda-Klimaszewski 

Docker for Data Engineering: Postgres, Docker Compose, and Real-World Workflows

How modern ML workflows evolve from human-supervised pipelines to scalable, agent-driven feedback loops — with concrete examples and real-world transitions.

​Outline:

1. ​**Human-in-the-Loop (HITL): Foundations and Limitations**
* ​What HITL means in practice (guided feedback loops, evaluation, corrections).
* ​Key limitations: scalability, latency, reasoning gaps, context sensitivity.
* ​Where HITL still shines: accuracy, safety, human effectiveness.
2. ​**Agent-in-the-Loop (AITL): The Next Evolution Step**
* ​What changes when agents become active participants in the loop.
* ​Architectural view: planning, tool-use, continuous improvement, automation.
* ​Strengths: adaptivity, speed, scalable labeling, generalization.
3. ​**Real-World Comparison & When Each Paradigm Wins**
* ​HITL vs AITL across accuracy, trust, cost, transparency, and scalability.
* ​HITL-critical domains: medical, autonomous driving, manufacturing.
* ​AITL-favored areas: fraud detection, recommender systems, logistics.
4. ​**Transition & Future Outlook**
* ​Human-in-the-loop → human-on-the-loop → human-over-the-loop.
* ​Hybrid approaches combining human judgment with agent autonomy.

​
​**Bio:**
Ertuğrul Mutlu is a Computer Engineering student at RWTH Aachen University and a Werkstudent Researcher at Fraunhofer IAIS (Enterprise Information Systems). His work spans reliable AI systems, agentic workflows, applied LLM engineering, and signal‑processing‑based feature extraction. He focuses on building practical, lightweight AI systems that bridge classical methods with modern LLM‑driven agent architectures. He recently published a preprint on wavelet‑based feature engineering and clustering, writes technical articles on [dev.to](http://dev.to/) about ML systems and agentic AI, and actively contributes to the open‑source and data/ML community through prototypes, research notes, and talks.

\*\*Join our slack: [https://datatalks.club/slack.html**](https://datatalks.club/slack.html**)

Baki Bektas

Vlad-Emil Petrea

Hui C

Mark

Manuel

Jaime Oscar Barrial Castillo

Torsten

Alidu Abubakari

smy misr

Melek Ergin

Jesper Gadeberg

Steffen

Claus

Senem

From Human-in-the-Loop to Agent-in-the-Loop: A Practical Transition Guide

**Exploring open source profitability and small language models – Aditya Gautam**

​Aditya Gautam has built his career at the intersection of AI research, large-scale deployment, and public discourse. With experience at Google, Meta, and leading academic conferences, he works on large language models, AI agents, and responsible AI at scale.

​In this episode, Aditya explores the debates around open-source AI, the economics behind LLMs, and the barriers enterprises face when adopting AI agents. He also shares his perspective on the rise of small language models and what these shifts mean for the future of AI.

​We plan to cover:

* ​Open-source AI: democratization and risks
* ​The economics of LLMs and the challenge of profitability
* ​Why enterprises struggle to adopt AI agents in practice
* ​The role of small language models in efficiency and cost reduction
* ​Emerging trends in AI research and deployment

​**About the Guest**

​**Aditya Gautam** is an AI researcher and engineer whose work spans industrial innovation, academic research, and AI policy. He has held roles at Google and Meta, working on recommendation systems, integrity, and large-scale generative AI deployment. His research covers topics including misinformation, multi-agent systems, and LLM evaluation, and he has published in top-tier conferences such as ICWSM while serving as a peer reviewer for venues like NeurIPS, ICML, and AAAI.

​Aditya is also an active voice in the AI community: he speaks at industry events such as the Databricks Data + AI Summit and Analytics Vidhya, contributes to policy discussions around regulations like the EU Digital Services Act, and shares insights on the economics and practical adoption of LLMs and AI agents. He holds a Master’s degree from Carnegie Mellon University.

**[DataTalks.Club](http://datatalks.club/) is the place to talk about data. [Join our slack community](https://datatalks.club/slack.html)!**

AlexD

kumudu.bastian

Bowyer Christophe

Aftab

Audrius

Ramanand Bhagwandin

Olena Gorbatiuk

Yacine Touati

Jonas Lünendonk

Pavan Kumar

magdalena.kunat

devjutsu

Anita P

The Future of AI Agents

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