What we’re about
This group was looking for a new owner, so we at DataTalks.Club decided to take it.
We host weekly virtual meetups on AI, machine learning, system design, recsys, etc. Each meetup is a ~30-40 minute talk, followed by a Q&A.
Note: Messages in Meetup are not monitored.
Upcoming events
4
How to Reduce LLM Hallucinations with Wikidata: Hands-On Fact-Checking Using MCP
·OnlineOnlineLLMs are powerful, but they still hallucinate facts, especially when asked about entities, relationships, or claims that require up-to-date or structured knowledge.
In this hands-on workshop, we'll explore how to use Wikidata as a grounding and fact-checking layer for LLMs to reduce hallucinations and make AI systems more reliable.
We'll start with a short introduction to Wikidata and then set up the Wikidata MCP so an LLM can retrieve and verify facts rather than relying solely on its internal memory. This already provides a practical way to ground LLM outputs in verifiable data.
From there, we’ll go beyond LLM-only approaches and build a small experimental fact-checking pipeline. The system combines semantic retrieval, LLM-based reranking, and natural language inference (NLI) to validate claims against evidence in a more controlled and interpretable way.
This workshop focuses on evidence-driven verification pipelines that make LLM's reasoning steps explicit and easier to inspect, debug, and improve.
What we'll cover:
- Wikidata as a structured source for factual verification
- Setting up and querying Wikidata using MCP
- Verifying claims with MCP + an LLM
- Moving beyond pure GenAI to evidence-based fact-checking
- Finding relevant Wikidata statements with semantic search
- Ranking candidate evidence with an LLM
- Verifying claims using an NLI model
What you'll leave with
By the end of the workshop, you'll be able to:
- Ground LLM outputs in structured data to reduce hallucinations
- Understand when LLM-only fact-checking is not enough
- Build a small, transparent fact-checking pipeline you can adapt to real projects
About the speaker:
Philippe Saadé is the AI/ML project manager at Wikimedia Deutschland. His current work focuses on making Wikidata accessible to AI application with projects like the Wikidata vector database and the Wikidata Model Context Protocol.
**Join our Slack: https://datatalks.club/slack.html**
This event is sponsored by Wikimedia13 attendees
AI Engineering: Skill Stack, Agents, LLMOps, and How to Ship AI Products
·OnlineOnlineShipping real AI products is now one of the most in-demand engineering skills, but most teams still get stuck turning prototypes into something that actually works.
In this podcast, AI engineer and bestselling author Paul Iusztin breaks down the full AI engineering skill stack:
- The principles behind production-grade systems
- How to build reliable agentic workflows
- The LLMOps practices that keep models stable
- The patterns he’s learned from shipping more than 20 AI applications
We’ll also go beyond the code. Paul will share how he structures his work, teaching, writing, and professional growth, and how he uses AI tools to stay focused, productive, and consistent.
Join us live if you want a straightforward look at the technical and personal side of modern AI engineering.
About the Speaker:
Paul Iusztin is an AI engineer committed to helping developers create fully functional, production-grade AI products. He is the author of the bestselling "LLM Engineer’s Handbook," leads the Agentic AI Engineering course, and is a founding AI engineer at a startup based in San Francisco. He also Decoding AI Magazine, where he assists engineers in moving beyond the proof-of-concept stage to build more effective AI systems.
With over ten years of experience, Paul teaches comprehensive AI engineering, covering everything from data gathering to deployment, monitoring, and evaluation. He emphasizes robust software practices, infrastructure, and principles that are reliable in a world increasingly influenced by AI coding tools.
**Join our Slack: https://datatalks.club/slack.html**5 attendees
Analytics Engineering with dbt Workshop
·OnlineOnlineA Practical Introduction - Juan Manuel Perafan
In this hands-on workshop, we'll introduce you to analytics engineering with dbt and guide you through building your first data models.
We'll cover the following steps:
- Setting up a dbt project and connecting to your data source
- Writing SQL transformations and building layered data models
- Adding tests and documentation to maintain data quality
- Running and understanding the dbt workflow
By the end of this workshop, you'll understand how to use dbt to transform raw data into clean, documented datasets for analytics.
About the speaker:
Juan Manuel Perafan is an Instructor, Author, and Speaker who specializes in analytics engineering and dbt. He's the co-author of "Fundamentals of Analytics Engineering" and has taught analytics engineering to hundreds of data professionals through workshops, courses, and community events. Juan founded the Analytics Engineering Meetup NL and Dutch dbt Meetup, and has spoken at dozens of conferences and meetups around the world, including dbt Coalesce, Linux Foundation OS Summit, Big Data Summit Warsaw, Big Data Expo NL, and Developer Week Latin America.
**Join our Slack: https://datatalks.club/slack.html**8 attendees
The Future of AI Agents
·OnlineOnlineExploring 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 will explore 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 expert contributing to the advancement of the field through industrial innovation, academic research, and public discourse. At the core of his work, he designs and builds systems in the domain of Large Language Models (LLMs) and AI Agents. He has successfully led several high-impact applied AI initiatives at Meta, focusing on both enhancing core ranking and recommendation algorithms and successfully architecting, developing, and productionizing state-of-the-art GenAI systems at massive scale.
As an active voice in the Generative AI community, Aditya actively disseminates his expertise on the global stage. He was featured among the best speakers at the Databricks Data + AI Summit 2025 for his talk on "Optimize Cost and User Value Through Model Routing AI Agent." He has published research on LLMs in Information Retrieval and Multi-Agent Systems, and his thought leadership has been featured in major media articles, industry interviews, and AI podcasts. He is a frequent speaker and panelist at premier events and conferences such as Analytics Vidhya. His influence in the academic community is demonstrated by his service as a respected peer reviewer for top-tier venues like NeurIPS, ICML, and AAAI. Aditya holds a Master’s degree from Carnegie Mellon University.
**Join our Slack: https://datatalks.club/slack.html**19 attendees
Past events
25
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