Über uns
A group for experienced and aspiring data professionals.
Join our Slack: https://datatalks.club/slack.html
Kommende Veranstaltungen
4
•OnlineDocker for Data Engineering: Postgres, Docker Compose, and Real-World Workflows
OnlineAlexey 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, 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.
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
57 Teilnehmer
•OnlineFrom Human-in-the-Loop to Agent-in-the-Loop: A Practical Transition Guide
OnlineHow modern ML workflows evolve from human-supervised pipelines to scalable, agent-driven feedback loops — with concrete examples and real-world transitions.
Outline:
- 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.
- 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.
- 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.
- 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 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**24 Teilnehmer
•OnlineThe Future of AI Agents
OnlineExploring 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 is the place to talk about data. Join our slack community!66 Teilnehmer
Vergangene Veranstaltungen
354
