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REGISTER BELOW FOR MORE AVAILABLE DATES! ↓↓↓↓↓ https://luma.com/stelios

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Who is this for?

​Students, developers, and anyone interested in using Large Language Models (LLMs) to build real software solutions with ** Python.

​Tired of vibe coding with AI tools? Want to actually understand and own your code, instead of relying on black-box magic? This session shows you how to build LLM systems properly, with full control and clear engineering principles. ​ Who is leading the session?

​The session is led by Dr. Stelios Sotiriadis, CEO of Warestack, Associate Professor and MSc Programme Director at Birkbeck, University of London, specialising in cloud computing, distributed systems, and AI engineering.

​Stelios holds a PhD from the University of Derby, completed a postdoctoral fellowship at the University of Toronto, and has worked on industry and research projects with Huawei, IBM, Autodesk, and multiple startups. Since moving to London in 2018, he has been teaching at Birkbeck. In 2021, he founded Warestack, building software for startups around the world. ​ What we’ll cover?

​A hands-on introduction to building software with LLMs using Python, Ollama, and LiteLLM, including:

• ​How LLMs, embeddings, and agents work.
• ​Calling local models with Ollama or cloud models (OpenAI, Gemini and more).
• ​Using LiteLLM for custom prompts and tool-calling.
• ​Building simple agents from scratch.
• ​Introduction to RAG (Retrieval-Augmented Generation).
• ​Working with vector databases (ChromaDB) and vector similarity search library (FAISS).
• ​Storing, searching, and retrieving embeddings.
• ​Introduction to Streamlit for interactive data apps.
• ​End-to-end examples you can run on your own machine.

​This session focuses on theory, fundamentals and real code you can re-use.

​Why LiteLLM?

​LiteLLM gives you low-level control to build custom LLM solutions your own way, without a heavy framework like LangChain, so you understand how everything works and design your own architecture. A dedicated LangChain session will follow for those who want to go further.

​What are the requirements?

​Bring a laptop with Python installed (Windows, macOS, or Linux), along with Visual Studio Code or a similar IDE, with at least 10GB of free disk space and 8GB of RAM.

​This space is needed for running local models during the workshop. ​If you don’t have a suitable laptop, please contact Stelios ([email protected]) before registering.

​What is the format?

​A 3-hour live session with:

• ​Interactive theory blocks
• ​Hands-on coding
• ​Step-by-step exercises
• ​Small group support
• ​Three 10-minute breaks
• ​Q&A and class quizzes

​This is a highly practical, hands-on class focused on code and building working LLM systems.

​What are the prerequisites?

​A good understanding of programming with Python is required (basic to intermediate level). I assume you are already comfortable writing Python scripts.

​What comes after?

​Participants will receive an optional mini capstone project with one-to-one personalised feedback.

​Is it just one session?

​This is the first session in a new sequence on applied AI, covering agents, RAG systems, vector databases, and production-ready LLM workflows. Later sessions will dive deeper into topics such as embeddings with deep neural networks, LangChain, advanced retrieval, and multi-agent architectures.

​You can decide afterwards whether you’d like to join future sessions.

​How many participants?

​To keep this interactive, only 15 spots are available. Please register as soon as possible.

End of Year Special: UX Design for Power BI + Building Data Foundations for GenAI!

We’re rounding off the year with a big one 🎉 Join us for a special in-person & hybrid session at Platform (Leeds Train Station) hosted by Hopton Analytics — complete with food, drinks, networking and two fantastic talks that bring together practical Power BI design, data strategy, and GenAI enablement.

Session 1:

Design That Works: How UX Principles Transform Power BI Reports Speaker: Simon Devine, Director at Hopton Analytics Good reporting isn’t just about visuals — it’s about communication. In this session, you’ll learn how UX thinking shapes better Power BI dashboards. We’ll explore:

• How users actually read and interpret data
• Practical layout & spacing techniques
• Effective typography and colour usage
• How to remove clutter and guide decision-making
• A real-time report makeover to demonstrate the transformation

If you've ever thought “This report makes sense… but it doesn’t feel right” — this session is for you.

Session 2:

Building Solid Data Foundations for GenAI Speaker: Maryleen Amaizu, Azure Data Platform Consultant AI works best when the data behind it is strong, reliable, and well-structured. Maryleen will walk through how to architect the underlying data environment needed to support Retrieval-Augmented Generation (RAG) for business use-cases. This session will include a demo using:

• AWS (Hosted GenAI Models)
• Python (RAG Logic)
• UI Layer for visualisation

Learn how to design systems that allow GenAI to actually deliver value rather than just buzzwords.

Food + Drinks + Great People \| In Person & Online 🎥 Whether you love geeking out over a semantic model, pushing Power BI design to the next level, or exploring GenAI — this is a must-attend event to round off the year.

RSVP now to secure your in-person spot — space is limited!

In our fourth Python + AI session, we'll explore one of the most popular techniques used with LLMs: Retrieval Augmented Generation.

RAG is an approach that sends context to the LLM so that it can provide well-grounded answers for a particular domain.

The RAG approach can be used with many kinds of data sources like CSVs, webpages, documents, databases. In this session, we'll walk through RAG flows in Python, starting with a simple flow and culminating in a full-stack RAG application based on Azure AI Search.

Pre-requisites: Habla español? Tendremos una serie para hispanohablantes!

In our fourth Python + AI session, we'll explore one of the most popular techniques used with LLMs: Retrieval Augmented Generation.

RAG is an approach that sends context to the LLM so that it can provide well-grounded answers for a particular domain.

The RAG approach can be used with many kinds of data sources like CSVs, webpages, documents, databases. In this session, we'll walk through RAG flows in Python, starting with a simple flow and culminating in a full-stack RAG application based on Azure AI Search.

Pre-requisites: Habla español? Tendremos una serie para hispanohablantes!

In our fourth Python + AI session, we'll explore one of the most popular techniques used with LLMs: Retrieval Augmented Generation.

RAG is an approach that sends context to the LLM so that it can provide well-grounded answers for a particular domain.

The RAG approach can be used with many kinds of data sources like CSVs, webpages, documents, databases. In this session, we'll walk through RAG flows in Python, starting with a simple flow and culminating in a full-stack RAG application based on Azure AI Search.

Pre-requisites: Habla español? Tendremos una serie para hispanohablantes!

Important: Register on the Event Website to receive joining link. (rsvp on meetup will NOT receive joining link).

Description: Welcome to the weekly AI virtual seminars, in collaboration with Oracle. Join us for deep dive tech talks on AI, hands-on experiences on code labs, workshops, and networking with speakers & fellow developers from all over the world.

Tech Talk: Develop an AI Agent Application using LangChain and Oracle Database 23ai Speakers: Blake Hendricks (Oracle) Abstract: AI agents are redefining the way enterprises interact with data enabling automated reasoning, complex decision-making, and seamless integration with external tools. This workshop provides a practical, hands-on journey into building AI agents using Python, LangChain, and Oracle Database 23ai. Attendees will learn how to design agents capable of: • Accessing and reasoning over relational and RAG (Retrieval-Augmented Generation) data in Oracle Database 23ai. • Integrating with external tools for tasks like sending emails or generating PDFs. • Maintaining conversational context and continuity for more natural, informed decision-making. Through guided exercises, participants will gain experience in creating production-ready AI agents for real-world enterprise scenarios. Venue: virtual, join from anywhere.

More upcoming sessions:

• Oct 28th: AI Webinar with Oracle (Ep 10) - Implementing AI Vector Search

Local and Global AI Community on Discord Join us on discord for local and global AI tech community:

• Events chat: chat and connect with speakers and global and local attendees;
• Learning AI: events, learning materials, study groups;
• Startups: innovation, projects collaborations, founders/co-founders;
• Jobs and Careers: job openings, post resumes, hiring managers

Important: Register on the event website to receive joining link. (rsvp on meetup will NOT receive joining link). If you can't make to the live session, still register to receive recordings.

Description: Welcome to the weekly AI virtual seminars, in collaboration with Oracle. Join us for deep dive tech talks on AI, hands-on experiences on code labs, workshops, and networking with speakers & fellow developers from all over the world.

Tech Talk: Develop an AI Agent Application using LangChain and Oracle Database 23ai Speakers: Blake Hendricks (Oracle) Abstract: AI agents are redefining the way enterprises interact with data—enabling automated reasoning, complex decision-making, and seamless integration with external tools. This workshop provides a practical, hands-on journey into building AI agents using Python, LangChain, and Oracle Database 23ai. Attendees will learn how to design agents capable of: • Accessing and reasoning over relational and RAG (Retrieval-Augmented Generation) data in Oracle Database 23ai. • Integrating with external tools for tasks like sending emails or generating PDFs. • Maintaining conversational context and continuity for more natural, informed decision-making. Through guided exercises, participants will gain experience in creating production-ready AI agents for real-world enterprise scenarios.

More Virtual Sessions:

• Oct 10th: AI DevTalks Live with Google Cloud - Ep 4
• Oct 14th: AI Webinar with Docker - Building event-driven agents
• Oct 28th: AI Webinar with Oracle (Ep 10) - Implementing AI Vector Search
• Nov 4th: NVIDIA FLARE Webinar 4 - Federated Learning from Industrial Perspectives

Important: Register on the Event Website to receive joining link. (rsvp on meetup will NOT receive joining link).

Description: Welcome to the weekly AI virtual seminars, in collaboration with Oracle. Join us for deep dive tech talks on AI, hands-on experiences on code labs, workshops, and networking with speakers & fellow developers from all over the world.

Tech Talk: Develop an AI Agent Application using LangChain and Oracle Database 23ai Speakers: Blake Hendricks (Oracle) Abstract: AI agents are redefining the way enterprises interact with data—enabling automated reasoning, complex decision-making, and seamless integration with external tools. This workshop provides a practical, hands-on journey into building AI agents using Python, LangChain, and Oracle Database 23ai. Attendees will learn how to design agents capable of: • Accessing and reasoning over relational and RAG (Retrieval-Augmented Generation) data in Oracle Database 23ai. • Integrating with external tools for tasks like sending emails or generating PDFs. • Maintaining conversational context and continuity for more natural, informed decision-making. Through guided exercises, participants will gain experience in creating production-ready AI agents for real-world enterprise scenarios. Venue: virtual, join from anywhere. More upcoming sessions:

• Oct 28th: AI Webinar with Oracle (Ep 10) - Implementing AI Vector Search

Local and Global AI Community on Discord Join us on discord for local and global AI tech community:

• Events chat: chat and connect with speakers and global and local attendees;
• Learning AI: events, learning materials, study groups;
• Startups: innovation, projects collaborations, founders/co-founders;
• Jobs and Careers: job openings, post resumes, hiring managers.

Important: Register on the Event Website to receive joining link. (rsvp on meetup will NOT receive joining link).

Description: Welcome to the weekly AI virtual seminars, in collaboration with Oracle. Join us for deep dive tech talks on AI, hands-on experiences on code labs, workshops, and networking with speakers & fellow developers from all over the world.

Tech Talk: Develop an AI Agent Application using LangChain and Oracle Database 23ai Speakers: Blake Hendricks (Oracle) Abstract: AI agents are redefining the way enterprises interact with data enabling automated reasoning, complex decision-making, and seamless integration with external tools. This workshop provides a practical, hands-on journey into building AI agents using Python, LangChain, and Oracle Database 23ai. Attendees will learn how to design agents capable of: • Accessing and reasoning over relational and RAG (Retrieval-Augmented Generation) data in Oracle Database 23ai. • Integrating with external tools for tasks like sending emails or generating PDFs. • Maintaining conversational context and continuity for more natural, informed decision-making. Through guided exercises, participants will gain experience in creating production-ready AI agents for real-world enterprise scenarios. Venue:

More upcoming sessions:

• Oct 28th: AI Webinar with Oracle (Ep 10) - Implementing AI Vector Search

Local and Global AI Community on Discord Join us on discord for local and global AI tech community:

• Events chat: chat and connect with speakers and global and local attendees;
• Learning AI: events, learning materials, study groups;
• Startups: innovation, projects collaborations, founders/co-founders;
• Jobs and Careers: job openings, post resumes, hiring managers.

Important: Register on the Event Website to receive joining link. (rsvp on meetup will NOT receive joining link).

Description: Welcome to the weekly AI virtual seminars, in collaboration with Oracle. Join us for deep dive tech talks on AI, hands-on experiences on code labs, workshops, and networking with speakers & fellow developers from all over the world.

Tech Talk: Develop an AI Agent Application using LangChain and Oracle Database 23ai Speakers: Blake Hendricks (Oracle) Abstract: AI agents are redefining the way enterprises interact with data enabling automated reasoning, complex decision-making, and seamless integration with external tools. This workshop provides a practical, hands-on journey into building AI agents using Python, LangChain, and Oracle Database 23ai. Attendees will learn how to design agents capable of: • Accessing and reasoning over relational and RAG (Retrieval-Augmented Generation) data in Oracle Database 23ai. • Integrating with external tools for tasks like sending emails or generating PDFs. • Maintaining conversational context and continuity for more natural, informed decision-making. Through guided exercises, participants will gain experience in creating production-ready AI agents for real-world enterprise scenarios. Venue: virtual, join from anywhere.

More upcoming sessions:

• Oct 28th: AI Webinar with Oracle (Ep 10) - Implementing AI Vector Search

Local and Global AI Community on Discord Join us on discord for local and global AI tech community:

• Events chat: chat and connect with speakers and global and local attendees;
• Learning AI: events, learning materials, study groups;
• Startups: innovation, projects collaborations, founders/co-founders;
• Jobs and Careers: job openings, post resumes, hiring managers

Take your AI applications to the next level by mastering GraphRAG in this exclusive Neo4j virtual workshop. Discover how to use the neo4j-graphrag open-source python package to build and optimize Retrieval-Augmented Generation (RAG) systems seamlessly integrated with generative AI models.

Take your AI applications to the next level by mastering GraphRAG in this exclusive Neo4j virtual workshop. Discover how to use the neo4j-graphrag open-source python package to build and optimize Retrieval-Augmented Generation (RAG) systems seamlessly integrated with generative AI models.

Take your AI applications to the next level by mastering GraphRAG in this exclusive Neo4j virtual workshop. Discover how to use the neo4j-graphrag open-source python package to build and optimize Retrieval-Augmented Generation (RAG) systems seamlessly integrated with generative AI models.

Take your AI applications to the next level by mastering GraphRAG in this exclusive Neo4j virtual workshop. Discover how to use the neo4j-graphrag open-source python package to build and optimize Retrieval-Augmented Generation (RAG) systems seamlessly integrated with generative AI models.

To attend, you must enroll here: https://lu.ma/7j80z56c

​Get hands-on with the next generation of AI tools in this technical workshop focused on building a company analysis AI agent. You'll learn how to:

• ​Orchestrate multi-step AI workflows using the BeeAI framework
• ​Integrate real-time internet search using the Tavily MCP server
• ​Add RAG (Retrieval-Augmented Generation) capabilities with Redis to search document sets

​Whether you're building agents for internal tools, research, or production use, this is your chance to work directly with the teams behind these cutting-edge tools. ​🍕 Pizza and refreshments will be provided.

Seats are limited — RSVP now to secure your spot! ​🎯 Who Should Attend ​This workshop is designed for:

• ​Developers & Data Scientists
• ​Technical Product Managers
• ​Engineering Students with Python experience
• ​Technical hobbyists forever learning

​To get the most out of the workshop, you'll need: ✅ A working knowledge of Python ✅ Comfort using your preferred IDE ✅ A laptop you can code on during the session ​

Join us at PyData Milton Keynes for an insightful session on Generative AI and Retrieval-Augmented Generation (RAG) using Python. This event will explore how to combine the power of large language models with dynamic information retrieval to build intelligent, context-aware systems. We’ll cover the fundamentals of RAG, walk through practical implementations using open-source tools like Hugging Face, LangChain and discuss real-world use cases such as document Q&A and enterprise search. Whether you're just starting out with LLMs or looking to deploy advanced AI solutions, this session will provide valuable knowledge and inspiration. Don’t miss this opportunity to deepen your understanding of GenAI and connect with fellow data professionals in the PyData community.

Summary In this episode of the Data Engineering Podcast Kacper Łukawski from Qdrant about integrating MCP servers with vector databases to process unstructured data. Kacper shares his experience in data engineering, from building big data pipelines in the automotive industry to leveraging large language models (LLMs) for transforming unstructured datasets into valuable assets. He discusses the challenges of building data pipelines for unstructured data and how vector databases facilitate semantic search and retrieval-augmented generation (RAG) applications. Kacper delves into the intricacies of vector storage and search, including metadata and contextual elements, and explores the evolution of vector engines beyond RAG to applications like semantic search and anomaly detection. The conversation covers the role of Model Context Protocol (MCP) servers in simplifying data integration and retrieval processes, highlighting the need for experimentation and evaluation when adopting LLMs, and offering practical advice on optimizing vector search costs and fine-tuning embedding models for improved search quality.

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementData migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details.Your host is Tobias Macey and today I'm interviewing Kacper Łukawski about how MCP servers can be paired with vector databases to streamline processing of unstructured dataInterview IntroductionHow did you get involved in the area of data management?LLMs are enabling the derivation of useful data assets from unstructured sources. What are the challenges that teams face in building the pipelines to support that work?How has the role of vector engines grown or evolved in the past ~2 years as LLMs have gained broader adoption?Beyond its role as a store of context for agents, RAG, etc. what other applications are common for vector databaes?In the ecosystem of vector engines, what are the distinctive elements of Qdrant?How has the MCP specification simplified the work of processing unstructured data?Can you describe the toolchain and workflow involved in building a data pipeline that leverages an MCP for generating embeddings?helping data engineers gain confidence in non-deterministic workflowsbringing application/ML/data teams into collaboration for determining the impact of e.g. chunking strategies, embedding model selection, etc.What are the most interesting, innovative, or unexpected ways that you have seen MCP and Qdrant used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on vector use cases?When is MCP and/or Qdrant the wrong choice?What do you have planned for the future of MCP with Qdrant?Contact Info LinkedInTwitter/XPersonal websiteParting Question From your perspective, what is the biggest gap in the tooling or technology for data management today?Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.init covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.Links QdrantKafkaApache OoziNamed Entity RecognitionGraphRAGpgvectorElasticsearchApache LuceneOpenSearchBM25Semantic SearchMCP == Model Context ProtocolAnthropic Contextualized ChunkingCohereThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

This book provides a comprehensive and practical guide to creating cutting-edge AI agents combining advanced technologies such as LLMs, retrieval-augmented generation (RAG), and knowledge graphs. By reading this book, you'll gain a deep understanding of how to design and build AI agents capable of real-world problem solving, reasoning, and action execution. What this Book will help me do Understand the foundations of LLMs, RAG, and knowledge graphs, and how they can be combined to build effective AI agents. Learn techniques to enhance factual accuracy and grounding through RAG pipelines and knowledge graphs. Develop AI agents that integrate planning, reasoning, and live tool usage to solve complex problems. Master the use of Python and popular AI libraries to build scalable AI agent applications. Acquire strategies for deploying and monitoring AI agents in production for reliable operation. Author(s) This book is written by Salvatore Raieli and Gabriele Iuculano, accomplished experts in artificial intelligence and machine learning. Both authors bring extensive professional experience from their work in AI-related fields, particularly in applying innovative AI methods to solve challenging problems. Through their clear and approachable writing style, they aim to make advanced AI concepts accessible to readers at various levels. Who is it for? This book is ideally suited for data scientists, AI practitioners, and technology enthusiasts seeking to deepen their knowledge in building intelligent AI agents. It is perfect for those who already have a foundational understanding of Python and general artificial intelligence concepts. Experienced professionals looking to explore state-of-the-art AI solutions, as well as beginners eager to advance their technical skills, will find this book invaluable.

Register: https://www.meetup.com/bliss-speaker-series/events/308415769/

Generative AI models have the potential to increase productivity and provide access to data, but they need good context to be truly useful. ​In this hands-on workshop, you will learn how Knowledge Graphs and Retrieval Augmented Generation (RAG) can help your GenAI projects avoid hallucination and provide access to reliable data. ​In this hands-on workshop, you will: ​- Learn about Large Language Models (LLMs), hallucination and integrating knowledge graphs ​- Explore Retrieval Augmented Generation (RAG) and GraphRAG and their role in ​- Use vector indexes and embeddings to find similar data ​- Query graphs using natural language ​- Use Python and OpenAI to create GraphRAG retrievers and GenAI applications ​This workshop will put you on the path to controlling Generative AI applications and integrating them into your projects.

A hands-on workshop on turning retrieval-augmented generation into agentic AI flows - Alexey Grigorev

​Description: ​Retrieval-Augmented Generation (RAG) is a common approach for building AI assistants, but most implementations stop at a single search + answer pattern. In this practical workshop, we go a step further.

​You'll learn how to make RAG pipelines more agentic: enabling decision-making, tool use, multi-step reasoning, and real-time interaction with users. We’ll build step by step, from simple retrieval to agent-powered assistants capable of asking follow-up questions, running tool calls, and updating their own knowledge base.

​What we’ll cover:

• ​Build a basic RAG pipeline: Implement search + prompt + LLM logic using course FAQs
• ​Add agentic behavior - ​Let the assistant decide when to search or answer - ​Introduce state\, reasoning\, and multi-step flows
• ​Implement agentic search - Iterate over multiple search queries - Use search history and reasoning to refine results
• ​Use OpenAI function calling - Add tool support: search and FAQ updates - Maintain interaction history and responses
• ​Build a working assistant - Organize your logic with chat_assistant.py - Interact with a live\, modular AI assistant
• ​Explore PydanticAI - Define tools with type hints and docstrings - Run your assistant in a fully typed agent framework

​What you’ll get: ​A working RAG-based AI assistant that can:

• ​Search FAQs
• ​Decide when to act
• ​Ask follow-up questions
• ​Add entries to its own knowledge base

​Everything runs in Jupyter with Python. You can follow along with GitHub Codespaces or use your own environment. ​ About the Speaker: ​Alexey Grigorev is the creator of DataTalks.Club and instructor of the popular Zoomcamp series. He designs and teaches practical AI/ML workflows used by thousands of learners globally. In this workshop, Alexey brings his hands-on teaching style to the emerging field of agentic LLMs, helping you build useful assistants from scratch.

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