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Title & Speakers	Event
Vector Space Day 2025-09-26 · 07:00 Register: https://luma.com/p7w9uqtz?tk=XlH6v1 Vector Space Day is bringing together engineers, researchers, and AI builders to explore the cutting edge of retrieval, vector search infrastructure, and agentic AI. We’re excited that Martin O'Hanlon from Neo4j will be speaking! His session, Hands On with GraphRAG, is a practical exploration of how graphs strengthen RAG. Attendees will learn how to: -Build a Knowledge Graph from unstructured data -Use GraphRAG to provide grounding and context for generative AI -Improve vector retrieval with graph structures -Integrate GraphRAG tools into a LangChain agent Join us in Berlin for a hands-on look at how Knowledge Graphs + RAG create more reliable and context-aware AI applications:	Vector Space Day
Polyglot RAG: Building a Multimodal, Multilingual, and Agentic AI Assistant 2025-07-10 · 18:25 Axel Sirota AI assistants are evolving from simple Q&A bots to intelligent, multimodal, multilingual, and agentic systems capable of reasoning, retrieving, and autonomously acting. In this talk, we’ll showcase how to build a voice-enabled, multilingual, multimodal RAG (Retrieval-Augmented Generation) assistant using Gradio, OpenAI’s Whisper, LangChain, LangGraph, and FAISS. Our assistant will not only process voice and text inputs in multiple languages but also intelligently retrieve information from structured and unstructured data. We’ll demonstrate this with a flight search use case—leveraging a flight database for retrieval and, when necessary, autonomously searching external sources using LangGraph. You will gain practical insights into building scalable, adaptive AI assistants that move beyond static chatbots to autonomous agents that interact dynamically with users and the web. AI/ML LLM RAG	SciPy 2025
Sponsored by: Qubika \| Agentic AI In Finance: How To Build Agents Using Databricks And LangGraph 2025-06-11 · 23:30 Sebastian Diaz – SVP of Data & AI @ Qubika Join us for this session on how to build AI finance agents with Databricks and LangChain. This session introduces a powerful approach to building AI agents by combining a modular framework that integrates LangChain, retrieval-augmented generation (RAG), and Databricks' unified data platform to build intelligent, adaptable finance agents. We’ll walk through the architecture and key components, including Databricks Unity Catalog, ML Flow, and Mosaic AI involved in building a system tailored for complex financial tasks like portfolio analysis, reporting automation, and real-time risk insights. We’ll also showcase a demo of one such agent in action - a Financial Analyst Agent. This agent emulates the expertise of a seasoned data analyst, delivering in-depth analysis in seconds - eliminating the need to wait hours or days for manual reports. The solution provides organizations with 24/7 access to advanced data analysis, enabling faster, smarter decision-making. AI/ML Databricks LLM RAG	Data + AI Summit 2025 Video
An Evolving DAG for the LLM world - Julia Schottenstein of LangChain at Small Data SF 2025-05-03 · 23:03 Julia Schottenstein – Product Manager @ dbt labs Directed Acyclic Graphs (DAGs) are the foundation of most orchestration frameworks. But what happens when you allow an LLM to act as the router? Acyclic graphs now become cyclic, which means you have to design for the challenges resulting from all this extra power. We'll cover the ins and outs of agentic applications and how to best use them in your work as a data practitioner or developer building today. ➡️ Follow Us LinkedIn: https://www.linkedin.com/company/small-data-sf/ X/Twitter : https://twitter.com/smalldatasf Website: https://www.smalldatasf.com/ Discover LangChain, the open-source framework for building powerful agentic systems. Learn how to augment LLMs with your private data, moving beyond their training cutoffs. We'll break down how LangChain uses "chains," which are essentially Directed Acyclic Graphs (DAGs) similar to data pipelines you might recognize from dbt. This structure is perfect for common patterns like Retrieval Augmented Generation (RAG), where you orchestrate steps to fetch context from a vector database and feed it to an LLM to generate an informed response, much like preparing data for analysis. Dive into the world of AI agents, where the LLM itself determines the application's control flow. Unlike a predefined DAG, this allows for dynamic, cyclic graphs where an agent can iterate and improve its response based on previous attempts. We'll explore the core challenges in building reliable agents: effective planning and reflection, managing shared memory across multiple agents in a cognitive architecture, and ensuring reliability against task ambiguity. Understand the critical trade-offs between the dependability of static chains and the flexibility of dynamic LLM agents. Introducing LangGraph, a framework designed to solve the agent reliability problem by balancing agent control with agency. Through a live demo in LangGraph Studio, see how to build complex AI applications using a cyclic graph. We'll demonstrate how a router agent can delegate tasks, execute a research plan with multiple steps, and use cycles to iterate on a problem. You'll also see how human-in-the-loop intervention can steer the agent for improved performance, a critical feature for building robust and observable agentic systems. Explore some of the most exciting AI agents in production today. See how Roblox uses an AI assistant to generate virtual worlds from a prompt, how TripAdvisor’s agent acts as a personal travel concierge to create custom itineraries, and how Replit’s coding agent automates code generation and pull requests. These real-world examples showcase the practical power of moving from simple DAGs to dynamic, cyclic graphs for solving complex, agentic problems. AI/ML dbt LLM RAG Vector DB	Small Data SF 2024 YouTube
The AI Showdown: Python, Rust & The Future of RAG 2025-03-25 · 18:30 Details Futures Forum is back in London! Explore the cutting-edge of Retrieval-Augmented Generation (RAG) through an exciting showdown between two powerful ecosystems: Python and Rust. Join us as we unpack the strengths and trade-offs of each language, illuminating the path forward in AI development. Whether you’re a seasoned Rust advocate, a Python enthusiast curious about what’s next, or an AI developer exploring your options, this event promises valuable insights and vibrant discussions about the future of AI tooling. Talks RAG Pipelines with Rig, Rust & SurrealDB - Joshua Mo\, DevRel Engineer @ Playgrounds RAG apps using Python, SurrealDB and Streamlit - Alexander Fridriksson\, Senior Product Marketing Manager @ SurrealDB Agenda 18:30 - 19:00 Welcome drinks & networking Grab a drink, explore the space and meet the SurrealDB team. 19:00 - 19:30 RAG Pipelines with Rig, Rust & SurrealDB Interested in Rust for AI? In this talk, we'll dive into how you can write RAG pipelines using Rig, a Rust AI framework that aims to prioritize developer experience. We'll also be discussing the state of the Rust AI/ML ecosystem. 19:30 - 20:00 Refreshments, pizza and networking 20:00 - 20:30 RAG apps using Python, SurrealDB and Streamlit This talk walks you through why LangChain might be weighing you down and how you can use pure Python - along with SurrealDB and Streamlit - to make production-ready AI apps effortlessly. 20:30 - 21:00 Wrap up & Networking 21:00 End of event -- About the speakers Joshua Mo - Joshua Mo is a DevRel Engineer at Playgrounds Analytics inc. He is currently maintaining the Rig agentic AI framework, as well as writing high-impact technical content to help drive Rust adoption. In his spare time, he contributes to FOSS software. Alexander Fridriksson - Alexander is a Senior Product Marketing Manager at SurrealDB\, where he's been involved with both the product and community from the start. Building out much of the messaging and education\, such as SurrealDB University. He has a wealth of experience from both a commercial (Marketing\, Sales\, Consulting) and Technical ( Analytics\, Data Science\, Data Engineering) perspective\, which he draws upon to help people solve the most challenging problems. FAQs Is the venue accessible? Absolutely! There is a lift that takes you up to Level 4 where the event is held. Is this event for me? SurrealDB events are for software engineers, developers, architects, data scientists, data engineers, or any tech professionals keen to discover more about SurrealDB: a scalable multi-model database that allows users and developers to focus on building their applications with ease and speed. Am I guaranteed a ticket at this event? Our events are tech-focused and in the interest of keeping our events relevant and meaningful for those attending, tickets are issued at our discretion. We therefore reserve the right to refund ticket orders before the event and to request proof of identity and/or professional background upon entry. Are there any House Rules? At SurrealDB, we are committed to providing live and online events that are safe and enjoyable for all attending. Please review our Code of Conduct and Privacy Policy for more information. It is compulsory for all attendees to be registered with a first and last name in order to attend. Any attendees who do not adhere to these requirements will be refused a ticket.	The AI Showdown: Python, Rust & The Future of RAG
The AI Showdown: Python, Rust & The Future of RAG 2025-03-25 · 18:30 Details Futures Forum is back in London! Explore the cutting-edge of Retrieval-Augmented Generation (RAG) through an exciting showdown between two powerful ecosystems: Python and Rust. Join us as we unpack the strengths and trade-offs of each language, illuminating the path forward in AI development. Whether you’re a seasoned Rust advocate, a Python enthusiast curious about what’s next, or an AI developer exploring your options, this event promises valuable insights and vibrant discussions about the future of AI tooling. Talks RAG Pipelines with Rig, Rust & SurrealDB - Joshua Mo\, DevRel Engineer @ Playgrounds RAG apps using Python, SurrealDB and Streamlit - Alexander Fridriksson\, Senior Product Marketing Manager @ SurrealDB Agenda 18:30 - 19:00 Welcome drinks & networking Grab a drink, explore the space and meet the SurrealDB team. 19:00 - 19:30 RAG Pipelines with Rig, Rust & SurrealDB Interested in Rust for AI? In this talk, we'll dive into how you can write RAG pipelines using Rig, a Rust AI framework that aims to prioritize developer experience. We'll also be discussing the state of the Rust AI/ML ecosystem. 19:30 - 20:00 Refreshments, pizza and networking 20:00 - 20:30 RAG apps using Python, SurrealDB and Streamlit This talk walks you through why LangChain might be weighing you down and how you can use pure Python - along with SurrealDB and Streamlit - to make production-ready AI apps effortlessly. 20:30 - 21:00 Wrap up & Networking 21:00 End of event -- About the speakers Joshua Mo - Joshua Mo is a DevRel Engineer at Playgrounds Analytics inc. He is currently maintaining the Rig agentic AI framework, as well as writing high-impact technical content to help drive Rust adoption. In his spare time, he contributes to FOSS software. Alexander Fridriksson - Alexander is a Senior Product Marketing Manager at SurrealDB\, where he's been involved with both the product and community from the start. Building out much of the messaging and education\, such as SurrealDB University. He has a wealth of experience from both a commercial (Marketing\, Sales\, Consulting) and Technical ( Analytics\, Data Science\, Data Engineering) perspective\, which he draws upon to help people solve the most challenging problems. FAQs Is the venue accessible? Absolutely! There is a lift that takes you up to Level 4 where the event is held. Is this event for me? SurrealDB events are for software engineers, developers, architects, data scientists, data engineers, or any tech professionals keen to discover more about SurrealDB: a scalable multi-model database that allows users and developers to focus on building their applications with ease and speed. Am I guaranteed a ticket at this event? Our events are tech-focused and in the interest of keeping our events relevant and meaningful for those attending, tickets are issued at our discretion. We therefore reserve the right to refund ticket orders before the event and to request proof of identity and/or professional background upon entry. Are there any House Rules? At SurrealDB, we are committed to providing live and online events that are safe and enjoyable for all attending. Please review our Code of Conduct and Privacy Policy for more information. It is compulsory for all attendees to be registered with a first and last name in order to attend. Any attendees who do not adhere to these requirements will be refused a ticket.	The AI Showdown: Python, Rust & The Future of RAG
Learning LangChain 2025-02-14 Nuno Campos – author , Mayo Oshin – author If you're looking to build production-ready AI applications that can reason and retrieve external data for context-awareness, you'll need to master--;a popular development framework and platform for building, running, and managing agentic applications. LangChain is used by several leading companies, including Zapier, Replit, Databricks, and many more. This guide is an indispensable resource for developers who understand Python or JavaScript but are beginners eager to harness the power of AI. Authors Mayo Oshin and Nuno Campos demystify the use of LangChain through practical insights and in-depth tutorials. Starting with basic concepts, this book shows you step-by-step how to build a production-ready AI agent that uses your data. Harness the power of retrieval-augmented generation (RAG) to enhance the accuracy of LLMs using external up-to-date data Develop and deploy AI applications that interact intelligently and contextually with users Make use of the powerful agent architecture with LangGraph Integrate and manage third-party APIs and tools to extend the functionality of your AI applications Monitor, test, and evaluate your AI applications to improve performance Understand the foundations of LLM app development and how they can be used with LangChain data ai-ml artificial-intelligence-ai large-language-models-llms langchain AI/ML API Databricks JavaScript LLM Python RAG	O'Reilly AI & ML Books
Jan 30 - AI, Machine Learning and Computer Vision Meetup 2025-01-30 · 18:00 Register for the Zoom! Date and Time Jan 30, 2025 at 10 AM Pacific Swimming Upstream: Using Machine Vision to Create Sustainable Practices in Fisheries of the Future Fishing vessels are on track to generate 10 million hours of video footage annually, creating a massive machine learning operations challenge. At AI.Fish, we are building an end-to-end system enabling non-technical users to harness AI for catch monitoring and classification both on-board and in the cloud. This talk explores our journey in building these approachable systems and working toward answering an old question: How many fish are in the ocean? About the Speaker Orvis Evans is a Software Engineer at AI.Fish, where he co-architects ML-Ops pipelines and develops intuitive interfaces that make machine vision accessible to non-technical users. Drawing from his background in building interactive systems, he builds front-end applications and APIs that enable fisheries to process thousands of hours of footage without machine learning expertise. Scaling Semantic Segmentation with Blender Generating datasets for semantic segmentation can be time-intensive. Learn how to use Blender’s Python API to create diverse and realistic synthetic data with automated labels, saving time and improving model performance. Preview the topics to be discussed in this Medium post. About the Speaker Vincent Vandenbussche has a PhD in Physics, is an author, and Machine Learning Engineer with 10 years of experience in software engineering and machine learning. WACV 2025 - Elderly Action Recognition Challenge Join us for a quick update on the Elderly Action Recognition (EAR) Challenge, part of the Computer Vision for Smalls (CV4Smalls) Workshop at the WACV 2025 conference! This challenge focuses on advancing research in Activity of Daily Living (ADL) recognition, particularly within the elderly population, a domain with profound societal implications. Participants will employ transfer learning techniques with any architecture or model they want to use. For example, starting with a general human action recognition benchmark and fine-tuning models on a subset of data tailored to elderly-specific activities. Sign up for the EAR challenge and learn more. About the Speaker Paula Ramos, PhD is a Senior DevRel and Applied AI Research Advocate at Voxel51. Transforming Programming Ed: An AI-Powered Teaching Assistant for Scalable and Adaptive Learning The future of education lies in personalized and scalable solutions, especially in fields like computer engineering where complex concepts often challenge students. This talk introduces Lumina (AI Teaching Assistant), a cutting-edge agentic system designed to revolutionize programming education through its innovative architecture and teaching strategies. Built using OpenAI API, LangChain, RAG, and ChromaDB, Lumina employs an agentic, multi-modal framework that dynamically integrates course materials, technical documentation, and pedagogical strategies into an adaptive knowledge-driven system. Its unique “Knowledge Components” approach decomposes programming concepts into interconnected teachable units, enabling proficiency-based learning and dynamic problem-solving guidance. Attendees will discover how Lumina’s agentic architecture enhances engagement, fosters critical thinking, and improves concept mastery, paving the way for scalable AI-driven educational solutions. About the Speaker Nittin Murthi Dhekshinamoorthy is a computer engineering student and researcher at the University of Illinois Urbana-Champaign with a strong focus on advancing artificial intelligence to solve real-world challenges in education and technology. He is the creator of an AI agent-based Teaching Assistant, leveraging cutting-edge frameworks to provide scalable, adaptive learning solutions, and has contributed to diverse, impactful projects, including natural language-to-SQL systems and deep learning models for clinical image segmentation.	Jan 30 - AI, Machine Learning and Computer Vision Meetup
Jan 30 - AI, Machine Learning and Computer Vision Meetup 2025-01-30 · 18:00 Register for the Zoom! Date and Time Jan 30, 2025 at 10 AM Pacific Swimming Upstream: Using Machine Vision to Create Sustainable Practices in Fisheries of the Future Fishing vessels are on track to generate 10 million hours of video footage annually, creating a massive machine learning operations challenge. At AI.Fish, we are building an end-to-end system enabling non-technical users to harness AI for catch monitoring and classification both on-board and in the cloud. This talk explores our journey in building these approachable systems and working toward answering an old question: How many fish are in the ocean? About the Speaker Orvis Evans is a Software Engineer at AI.Fish, where he co-architects ML-Ops pipelines and develops intuitive interfaces that make machine vision accessible to non-technical users. Drawing from his background in building interactive systems, he builds front-end applications and APIs that enable fisheries to process thousands of hours of footage without machine learning expertise. Scaling Semantic Segmentation with Blender Generating datasets for semantic segmentation can be time-intensive. Learn how to use Blender’s Python API to create diverse and realistic synthetic data with automated labels, saving time and improving model performance. Preview the topics to be discussed in this Medium post. About the Speaker Vincent Vandenbussche has a PhD in Physics, is an author, and Machine Learning Engineer with 10 years of experience in software engineering and machine learning. WACV 2025 - Elderly Action Recognition Challenge Join us for a quick update on the Elderly Action Recognition (EAR) Challenge, part of the Computer Vision for Smalls (CV4Smalls) Workshop at the WACV 2025 conference! This challenge focuses on advancing research in Activity of Daily Living (ADL) recognition, particularly within the elderly population, a domain with profound societal implications. Participants will employ transfer learning techniques with any architecture or model they want to use. For example, starting with a general human action recognition benchmark and fine-tuning models on a subset of data tailored to elderly-specific activities. Sign up for the EAR challenge and learn more. About the Speaker Paula Ramos, PhD is a Senior DevRel and Applied AI Research Advocate at Voxel51. Transforming Programming Ed: An AI-Powered Teaching Assistant for Scalable and Adaptive Learning The future of education lies in personalized and scalable solutions, especially in fields like computer engineering where complex concepts often challenge students. This talk introduces Lumina (AI Teaching Assistant), a cutting-edge agentic system designed to revolutionize programming education through its innovative architecture and teaching strategies. Built using OpenAI API, LangChain, RAG, and ChromaDB, Lumina employs an agentic, multi-modal framework that dynamically integrates course materials, technical documentation, and pedagogical strategies into an adaptive knowledge-driven system. Its unique “Knowledge Components” approach decomposes programming concepts into interconnected teachable units, enabling proficiency-based learning and dynamic problem-solving guidance. Attendees will discover how Lumina’s agentic architecture enhances engagement, fosters critical thinking, and improves concept mastery, paving the way for scalable AI-driven educational solutions. About the Speaker Nittin Murthi Dhekshinamoorthy is a computer engineering student and researcher at the University of Illinois Urbana-Champaign with a strong focus on advancing artificial intelligence to solve real-world challenges in education and technology. He is the creator of an AI agent-based Teaching Assistant, leveraging cutting-edge frameworks to provide scalable, adaptive learning solutions, and has contributed to diverse, impactful projects, including natural language-to-SQL systems and deep learning models for clinical image segmentation.	Jan 30 - AI, Machine Learning and Computer Vision Meetup
Jan 30 - AI, Machine Learning and Computer Vision Meetup 2025-01-30 · 18:00 Register for the Zoom! Date and Time Jan 30, 2025 at 10 AM Pacific Swimming Upstream: Using Machine Vision to Create Sustainable Practices in Fisheries of the Future Fishing vessels are on track to generate 10 million hours of video footage annually, creating a massive machine learning operations challenge. At AI.Fish, we are building an end-to-end system enabling non-technical users to harness AI for catch monitoring and classification both on-board and in the cloud. This talk explores our journey in building these approachable systems and working toward answering an old question: How many fish are in the ocean? About the Speaker Orvis Evans is a Software Engineer at AI.Fish, where he co-architects ML-Ops pipelines and develops intuitive interfaces that make machine vision accessible to non-technical users. Drawing from his background in building interactive systems, he builds front-end applications and APIs that enable fisheries to process thousands of hours of footage without machine learning expertise. Scaling Semantic Segmentation with Blender Generating datasets for semantic segmentation can be time-intensive. Learn how to use Blender’s Python API to create diverse and realistic synthetic data with automated labels, saving time and improving model performance. Preview the topics to be discussed in this Medium post. About the Speaker Vincent Vandenbussche has a PhD in Physics, is an author, and Machine Learning Engineer with 10 years of experience in software engineering and machine learning. WACV 2025 - Elderly Action Recognition Challenge Join us for a quick update on the Elderly Action Recognition (EAR) Challenge, part of the Computer Vision for Smalls (CV4Smalls) Workshop at the WACV 2025 conference! This challenge focuses on advancing research in Activity of Daily Living (ADL) recognition, particularly within the elderly population, a domain with profound societal implications. Participants will employ transfer learning techniques with any architecture or model they want to use. For example, starting with a general human action recognition benchmark and fine-tuning models on a subset of data tailored to elderly-specific activities. Sign up for the EAR challenge and learn more. About the Speaker Paula Ramos, PhD is a Senior DevRel and Applied AI Research Advocate at Voxel51. Transforming Programming Ed: An AI-Powered Teaching Assistant for Scalable and Adaptive Learning The future of education lies in personalized and scalable solutions, especially in fields like computer engineering where complex concepts often challenge students. This talk introduces Lumina (AI Teaching Assistant), a cutting-edge agentic system designed to revolutionize programming education through its innovative architecture and teaching strategies. Built using OpenAI API, LangChain, RAG, and ChromaDB, Lumina employs an agentic, multi-modal framework that dynamically integrates course materials, technical documentation, and pedagogical strategies into an adaptive knowledge-driven system. Its unique “Knowledge Components” approach decomposes programming concepts into interconnected teachable units, enabling proficiency-based learning and dynamic problem-solving guidance. Attendees will discover how Lumina’s agentic architecture enhances engagement, fosters critical thinking, and improves concept mastery, paving the way for scalable AI-driven educational solutions. About the Speaker Nittin Murthi Dhekshinamoorthy is a computer engineering student and researcher at the University of Illinois Urbana-Champaign with a strong focus on advancing artificial intelligence to solve real-world challenges in education and technology. He is the creator of an AI agent-based Teaching Assistant, leveraging cutting-edge frameworks to provide scalable, adaptive learning solutions, and has contributed to diverse, impactful projects, including natural language-to-SQL systems and deep learning models for clinical image segmentation.	Jan 30 - AI, Machine Learning and Computer Vision Meetup
GenAI Study Groups (Virtual): 7-Day Challenge for Building LLM Applications 2025-01-06 · 14:00 Important: Register on the event website to enroll the AI Study Group to start your learning journey (RSVP on meetup will NOT join). AISG (GenAI Study Group) is a learning opportunity leveraging books, papers, free online videos (youtube, coursera, deeplearning ai, etc..). It may include guided learning materials, hands-on assignments, tutoring, tech talks and group discussions. We are hosting a few study groups during the holiday, you can register to each of the groups: Jan 6~Jan 12: 7-Day Challenge for Building LLMs Applications Jan 9: AI Dev Tools Jan 13~Jan 27: 15-Day Challenge for Neural Networks Dec 27~Jan 4: AI Immersion: A Developer's Holiday Study Guide Jan 1~Jan 5: 5-Day Challenge for Agentic AI * Jan 6 \~ Jan 12: 7-Day Challenge for building LLMs application Join our study group for an exciting 7-day challenge to learn and build LLMs applications together! Each day features focused tasks, collaborative discussions, and hands-on coding exercises. By the end, you’ll have a solid foundation and even a working LLMs powered application. Let’s learn, create, and grow together! Study program structure Self paced learning materials with virtual meeting Daily course (30-60 minutes): Focused on specific technologies/concepts. Daily practice: Hands-on activities using the learned concepts. Daily workshop (30 minutes): To share learnings, troubleshoot challenges, and foster a learning community. Who is this study group for? Developers who wants to build LLMs powered applications. Schedule: Day 0: Kick off, information session, online meeting Day 1: Introduction to Prompt Engineering and LLM Fundamentals, self paced Day 2: Building Systems with the ChatGPT API, self paced Day 3: Introduction to LangChain for LLM Applications , self paced Day 4: RAG using LangChain , self paced Day 5: Functions, Tools, and Agents with LangChain, self paced Day 6: Use Case Tutorial and Demonstration, Online meeting Day 7: Conclusion, Final Project, Demo/showcase, Online meeting * Venue: virtual at Discord.	GenAI Study Groups (Virtual): 7-Day Challenge for Building LLM Applications
GenAI Study Groups (Virtual): 7-Day Challenge for Building LLM Applications 2025-01-06 · 09:00 Important: Register on the event website to enroll the AI Study Group to start your learning journey (RSVP on meetup will NOT join). AISG (GenAI Study Group) is a learning opportunity leveraging books, papers, free online videos (youtube, coursera, deeplearning ai, etc..). It may include guided learning materials, hands-on assignments, tutoring, tech talks and group discussions. We are hosting a few study groups during the holiday, you can register to each of the groups: Jan 6~Jan 12: 7-Day Challenge for Building LLMs Applications Jan 9: AI Dev Tools Jan 13~Jan 27: 15-Day Challenge for Neural Networks Dec 27~Jan 4: AI Immersion: A Developer's Holiday Study Guide Jan 1~Jan 5: 5-Day Challenge for Agentic AI * Jan 6 \~ Jan 12: 7-Day Challenge for building LLMs application Join our study group for an exciting 7-day challenge to learn and build LLMs applications together! Each day features focused tasks, collaborative discussions, and hands-on coding exercises. By the end, you’ll have a solid foundation and even a working LLMs powered application. Let’s learn, create, and grow together! Study program structure Self paced learning materials with virtual meeting Daily course (30-60 minutes): Focused on specific technologies/concepts. Daily practice: Hands-on activities using the learned concepts. Daily workshop (30 minutes): To share learnings, troubleshoot challenges, and foster a learning community. Who is this study group for? Developers who wants to build LLMs powered applications. Schedule: Day 0: Kick off, information session, online meeting Day 1: Introduction to Prompt Engineering and LLM Fundamentals, self paced Day 2: Building Systems with the ChatGPT API, self paced Day 3: Introduction to LangChain for LLM Applications , self paced Day 4: RAG using LangChain , self paced Day 5: Functions, Tools, and Agents with LangChain, self paced Day 6: Use Case Tutorial and Demonstration, Online meeting Day 7: Conclusion, Final Project, Demo/showcase, Online meeting * Venue: virtual at Discord.	GenAI Study Groups (Virtual): 7-Day Challenge for Building LLM Applications
Agentic RAG with Langchain 2024-09-10 · 17:00 RAG (Retrieval Augmented Generation) is the most common approach used to get LLMs to answer questions grounded in a particular domain's data. Learn how to use Langchain, the most popular Python package for LLM orchestration, to build a RAG application with Python and Azure OpenAI. Discover how to use agentic flows in Langchain to build more complex RAG workflows. Presented by Marlene Mhangami, Developer Advocate for Python Part of RAGHack, a free global hackathon to develop RAG applications. Join at https://aka.ms/raghack 📌 Check out the RAGHack 2024 series here! Pre-requisites: - Read the official rules and join the hack at https://aka.ms/raghack. No Purchase Necessary. Must be 18+ to enter. Contest ends 9/16/24. Want more hands-on RAG training? Visit the Reactor series home page to see all the RAGHack 2024 sessions!	Agentic RAG with Langchain
Agentic RAG with Langchain 2024-09-10 · 17:00 RAG (Retrieval Augmented Generation) is the most common approach used to get LLMs to answer questions grounded in a particular domain's data. Learn how to use Langchain, the most popular Python package for LLM orchestration, to build a RAG application with Python and Azure OpenAI. Discover how to use agentic flows in Langchain to build more complex RAG workflows. Presented by Marlene Mhangami, Developer Advocate for Python Part of RAGHack, a free global hackathon to develop RAG applications. Join at https://aka.ms/raghack 📌 Check out the RAGHack 2024 series here! Pre-requisites: - Read the official rules and join the hack at https://aka.ms/raghack. No Purchase Necessary. Must be 18+ to enter. Contest ends 9/16/24. Want more hands-on RAG training? Visit the Reactor series home page to see all the RAGHack 2024 sessions!	Agentic RAG with Langchain
Agentic RAG with Langchain 2024-09-10 · 17:00 RAG (Retrieval Augmented Generation) is the most common approach used to get LLMs to answer questions grounded in a particular domain's data. Learn how to use Langchain, the most popular Python package for LLM orchestration, to build a RAG application with Python and Azure OpenAI. Discover how to use agentic flows in Langchain to build more complex RAG workflows. Presented by Marlene Mhangami, Developer Advocate for Python Part of RAGHack, a free global hackathon to develop RAG applications. Join at https://aka.ms/raghack 📌 Check out the RAGHack 2024 series here! Pre-requisites: - Read the official rules and join the hack at https://aka.ms/raghack. No Purchase Necessary. Must be 18+ to enter. Contest ends 9/16/24. Want more hands-on RAG training? Visit the Reactor series home page to see all the RAGHack 2024 sessions!	Agentic RAG with Langchain
An Evolving DAG for the LLM world - Julia Schottenstein of LangChain at Small Data SF 2024-01-01 Directed Acyclic Graphs (DAGs) are the foundation of most orchestration frameworks. But what happens when you allow an LLM to act as the router? Acyclic graphs now become cyclic, which means you have to design for the challenges resulting from all this extra power. We'll cover the ins and outs of agentic applications and how to best use them in your work as a data practitioner or developer building today. ➡️ Follow Us LinkedIn: https://www.linkedin.com/company/small-data-sf/ X/Twitter : https://twitter.com/smalldatasf Website: https://www.smalldatasf.com/ Discover LangChain, the open-source framework for building powerful agentic systems. Learn how to augment LLMs with your private data, moving beyond their training cutoffs. We'll break down how LangChain uses "chains," which are essentially Directed Acyclic Graphs (DAGs) similar to data pipelines you might recognize from dbt. This structure is perfect for common patterns like Retrieval Augmented Generation (RAG), where you orchestrate steps to fetch context from a vector database and feed it to an LLM to generate an informed response, much like preparing data for analysis. Dive into the world of AI agents, where the LLM itself determines the application's control flow. Unlike a predefined DAG, this allows for dynamic, cyclic graphs where an agent can iterate and improve its response based on previous attempts. We'll explore the core challenges in building reliable agents: effective planning and reflection, managing shared memory across multiple agents in a cognitive architecture, and ensuring reliability against task ambiguity. Understand the critical trade-offs between the dependability of static chains and the flexibility of dynamic LLM agents. Introducing LangGraph, a framework designed to solve the agent reliability problem by balancing agent control with agency. Through a live demo in LangGraph Studio, see how to build complex AI applications using a cyclic graph. We'll demonstrate how a router agent can delegate tasks, execute a research plan with multiple steps, and use cycles to iterate on a problem. You'll also see how human-in-the-loop intervention can steer the agent for improved performance, a critical feature for building robust and observable agentic systems. Explore some of the most exciting AI agents in production today. See how Roblox uses an AI assistant to generate virtual worlds from a prompt, how TripAdvisor’s agent acts as a personal travel concierge to create custom itineraries, and how Replit’s coding agent automates code generation and pull requests. These real-world examples showcase the practical power of moving from simple DAGs to dynamic, cyclic graphs for solving complex, agentic problems. AI/ML dbt LLM RAG Vector DB	Small Data SF 2024 Video

Vector Space Day 2025-09-26 · 07:00

Register: https://luma.com/p7w9uqtz?tk=XlH6v1

Vector Space Day is bringing together engineers, researchers, and AI builders to explore the cutting edge of retrieval, vector search infrastructure, and agentic AI.

We’re excited that Martin O'Hanlon from Neo4j will be speaking!

His session, Hands On with GraphRAG, is a practical exploration of how graphs strengthen RAG. Attendees will learn how to: -Build a Knowledge Graph from unstructured data -Use GraphRAG to provide grounding and context for generative AI -Improve vector retrieval with graph structures -Integrate GraphRAG tools into a LangChain agent

Join us in Berlin for a hands-on look at how Knowledge Graphs + RAG create more reliable and context-aware AI applications:

Vector Space Day

Polyglot RAG: Building a Multimodal, Multilingual, and Agentic AI Assistant 2025-07-10 · 18:25

Axel Sirota

AI assistants are evolving from simple Q&A bots to intelligent, multimodal, multilingual, and agentic systems capable of reasoning, retrieving, and autonomously acting. In this talk, we’ll showcase how to build a voice-enabled, multilingual, multimodal RAG (Retrieval-Augmented Generation) assistant using Gradio, OpenAI’s Whisper, LangChain, LangGraph, and FAISS. Our assistant will not only process voice and text inputs in multiple languages but also intelligently retrieve information from structured and unstructured data. We’ll demonstrate this with a flight search use case—leveraging a flight database for retrieval and, when necessary, autonomously searching external sources using LangGraph. You will gain practical insights into building scalable, adaptive AI assistants that move beyond static chatbots to autonomous agents that interact dynamically with users and the web.

AI/ML LLM RAG

SciPy 2025

Sponsored by: Qubika | Agentic AI In Finance: How To Build Agents Using Databricks And LangGraph 2025-06-11 · 23:30

Sebastian Diaz – SVP of Data & AI @ Qubika

Join us for this session on how to build AI finance agents with Databricks and LangChain. This session introduces a powerful approach to building AI agents by combining a modular framework that integrates LangChain, retrieval-augmented generation (RAG), and Databricks' unified data platform to build intelligent, adaptable finance agents. We’ll walk through the architecture and key components, including Databricks Unity Catalog, ML Flow, and Mosaic AI involved in building a system tailored for complex financial tasks like portfolio analysis, reporting automation, and real-time risk insights. We’ll also showcase a demo of one such agent in action - a Financial Analyst Agent. This agent emulates the expertise of a seasoned data analyst, delivering in-depth analysis in seconds - eliminating the need to wait hours or days for manual reports. The solution provides organizations with 24/7 access to advanced data analysis, enabling faster, smarter decision-making.

AI/ML Databricks LLM RAG

Data + AI Summit 2025

Video

An Evolving DAG for the LLM world - Julia Schottenstein of LangChain at Small Data SF 2025-05-03 · 23:03

Julia Schottenstein – Product Manager @ dbt labs

Directed Acyclic Graphs (DAGs) are the foundation of most orchestration frameworks. But what happens when you allow an LLM to act as the router? Acyclic graphs now become cyclic, which means you have to design for the challenges resulting from all this extra power. We'll cover the ins and outs of agentic applications and how to best use them in your work as a data practitioner or developer building today.

➡️ Follow Us LinkedIn: https://www.linkedin.com/company/small-data-sf/ X/Twitter : https://twitter.com/smalldatasf Website: https://www.smalldatasf.com/

Discover LangChain, the open-source framework for building powerful agentic systems. Learn how to augment LLMs with your private data, moving beyond their training cutoffs. We'll break down how LangChain uses "chains," which are essentially Directed Acyclic Graphs (DAGs) similar to data pipelines you might recognize from dbt. This structure is perfect for common patterns like Retrieval Augmented Generation (RAG), where you orchestrate steps to fetch context from a vector database and feed it to an LLM to generate an informed response, much like preparing data for analysis.

Dive into the world of AI agents, where the LLM itself determines the application's control flow. Unlike a predefined DAG, this allows for dynamic, cyclic graphs where an agent can iterate and improve its response based on previous attempts. We'll explore the core challenges in building reliable agents: effective planning and reflection, managing shared memory across multiple agents in a cognitive architecture, and ensuring reliability against task ambiguity. Understand the critical trade-offs between the dependability of static chains and the flexibility of dynamic LLM agents.

Introducing LangGraph, a framework designed to solve the agent reliability problem by balancing agent control with agency. Through a live demo in LangGraph Studio, see how to build complex AI applications using a cyclic graph. We'll demonstrate how a router agent can delegate tasks, execute a research plan with multiple steps, and use cycles to iterate on a problem. You'll also see how human-in-the-loop intervention can steer the agent for improved performance, a critical feature for building robust and observable agentic systems.

Explore some of the most exciting AI agents in production today. See how Roblox uses an AI assistant to generate virtual worlds from a prompt, how TripAdvisor’s agent acts as a personal travel concierge to create custom itineraries, and how Replit’s coding agent automates code generation and pull requests. These real-world examples showcase the practical power of moving from simple DAGs to dynamic, cyclic graphs for solving complex, agentic problems.

AI/ML dbt LLM RAG Vector DB

Small Data SF 2024

YouTube

The AI Showdown: Python, Rust & The Future of RAG 2025-03-25 · 18:30

Details

Futures Forum is back in London!

Explore the cutting-edge of Retrieval-Augmented Generation (RAG) through an exciting showdown between two powerful ecosystems: Python and Rust. Join us as we unpack the strengths and trade-offs of each language, illuminating the path forward in AI development.

Whether you’re a seasoned Rust advocate, a Python enthusiast curious about what’s next, or an AI developer exploring your options, this event promises valuable insights and vibrant discussions about the future of AI tooling.

Talks

RAG Pipelines with Rig, Rust & SurrealDB - Joshua Mo\, DevRel Engineer @ Playgrounds
RAG apps using Python, SurrealDB and Streamlit - Alexander Fridriksson\, Senior Product Marketing Manager @ SurrealDB

Agenda

18:30 - 19:00 Welcome drinks & networking Grab a drink, explore the space and meet the SurrealDB team.

19:00 - 19:30 RAG Pipelines with Rig, Rust & SurrealDB Interested in Rust for AI? In this talk, we'll dive into how you can write RAG pipelines using Rig, a Rust AI framework that aims to prioritize developer experience. We'll also be discussing the state of the Rust AI/ML ecosystem.

19:30 - 20:00 Refreshments, pizza and networking

20:00 - 20:30 RAG apps using Python, SurrealDB and Streamlit This talk walks you through why LangChain might be weighing you down and how you can use pure Python - along with SurrealDB and Streamlit - to make production-ready AI apps effortlessly.

20:30 - 21:00 Wrap up & Networking

21:00 End of event

--

About the speakers

Joshua Mo - Joshua Mo is a DevRel Engineer at Playgrounds Analytics inc. He is currently maintaining the Rig agentic AI framework, as well as writing high-impact technical content to help drive Rust adoption. In his spare time, he contributes to FOSS software.
Alexander Fridriksson - Alexander is a Senior Product Marketing Manager at SurrealDB\, where he's been involved with both the product and community from the start. Building out much of the messaging and education\, such as SurrealDB University. He has a wealth of experience from both a commercial (Marketing\, Sales\, Consulting) and Technical ( Analytics\, Data Science\, Data Engineering) perspective\, which he draws upon to help people solve the most challenging problems.

FAQs

Is the venue accessible? Absolutely! There is a lift that takes you up to Level 4 where the event is held.

Is this event for me? SurrealDB events are for software engineers, developers, architects, data scientists, data engineers, or any tech professionals keen to discover more about SurrealDB: a scalable multi-model database that allows users and developers to focus on building their applications with ease and speed.

Am I guaranteed a ticket at this event? Our events are tech-focused and in the interest of keeping our events relevant and meaningful for those attending, tickets are issued at our discretion. We therefore reserve the right to refund ticket orders before the event and to request proof of identity and/or professional background upon entry.

Are there any House Rules? At SurrealDB, we are committed to providing live and online events that are safe and enjoyable for all attending. Please review our Code of Conduct and Privacy Policy for more information. It is compulsory for all attendees to be registered with a first and last name in order to attend. Any attendees who do not adhere to these requirements will be refused a ticket.

The AI Showdown: Python, Rust & The Future of RAG

The AI Showdown: Python, Rust & The Future of RAG 2025-03-25 · 18:30

Details

Futures Forum is back in London!

Explore the cutting-edge of Retrieval-Augmented Generation (RAG) through an exciting showdown between two powerful ecosystems: Python and Rust. Join us as we unpack the strengths and trade-offs of each language, illuminating the path forward in AI development.

Whether you’re a seasoned Rust advocate, a Python enthusiast curious about what’s next, or an AI developer exploring your options, this event promises valuable insights and vibrant discussions about the future of AI tooling.

Talks

RAG Pipelines with Rig, Rust & SurrealDB - Joshua Mo\, DevRel Engineer @ Playgrounds
RAG apps using Python, SurrealDB and Streamlit - Alexander Fridriksson\, Senior Product Marketing Manager @ SurrealDB

Agenda

18:30 - 19:00 Welcome drinks & networking Grab a drink, explore the space and meet the SurrealDB team.

19:00 - 19:30 RAG Pipelines with Rig, Rust & SurrealDB Interested in Rust for AI? In this talk, we'll dive into how you can write RAG pipelines using Rig, a Rust AI framework that aims to prioritize developer experience. We'll also be discussing the state of the Rust AI/ML ecosystem.

19:30 - 20:00 Refreshments, pizza and networking

20:00 - 20:30 RAG apps using Python, SurrealDB and Streamlit This talk walks you through why LangChain might be weighing you down and how you can use pure Python - along with SurrealDB and Streamlit - to make production-ready AI apps effortlessly.

20:30 - 21:00 Wrap up & Networking

21:00 End of event

--

About the speakers

Joshua Mo - Joshua Mo is a DevRel Engineer at Playgrounds Analytics inc. He is currently maintaining the Rig agentic AI framework, as well as writing high-impact technical content to help drive Rust adoption. In his spare time, he contributes to FOSS software.
Alexander Fridriksson - Alexander is a Senior Product Marketing Manager at SurrealDB\, where he's been involved with both the product and community from the start. Building out much of the messaging and education\, such as SurrealDB University. He has a wealth of experience from both a commercial (Marketing\, Sales\, Consulting) and Technical ( Analytics\, Data Science\, Data Engineering) perspective\, which he draws upon to help people solve the most challenging problems.

FAQs

Is the venue accessible? Absolutely! There is a lift that takes you up to Level 4 where the event is held.

Is this event for me? SurrealDB events are for software engineers, developers, architects, data scientists, data engineers, or any tech professionals keen to discover more about SurrealDB: a scalable multi-model database that allows users and developers to focus on building their applications with ease and speed.

Am I guaranteed a ticket at this event? Our events are tech-focused and in the interest of keeping our events relevant and meaningful for those attending, tickets are issued at our discretion. We therefore reserve the right to refund ticket orders before the event and to request proof of identity and/or professional background upon entry.

Are there any House Rules? At SurrealDB, we are committed to providing live and online events that are safe and enjoyable for all attending. Please review our Code of Conduct and Privacy Policy for more information. It is compulsory for all attendees to be registered with a first and last name in order to attend. Any attendees who do not adhere to these requirements will be refused a ticket.

The AI Showdown: Python, Rust & The Future of RAG

Learning LangChain 2025-02-14

Nuno Campos – author , Mayo Oshin – author

If you're looking to build production-ready AI applications that can reason and retrieve external data for context-awareness, you'll need to master--;a popular development framework and platform for building, running, and managing agentic applications. LangChain is used by several leading companies, including Zapier, Replit, Databricks, and many more. This guide is an indispensable resource for developers who understand Python or JavaScript but are beginners eager to harness the power of AI. Authors Mayo Oshin and Nuno Campos demystify the use of LangChain through practical insights and in-depth tutorials. Starting with basic concepts, this book shows you step-by-step how to build a production-ready AI agent that uses your data. Harness the power of retrieval-augmented generation (RAG) to enhance the accuracy of LLMs using external up-to-date data Develop and deploy AI applications that interact intelligently and contextually with users Make use of the powerful agent architecture with LangGraph Integrate and manage third-party APIs and tools to extend the functionality of your AI applications Monitor, test, and evaluate your AI applications to improve performance Understand the foundations of LLM app development and how they can be used with LangChain

data ai-ml artificial-intelligence-ai large-language-models-llms langchain AI/ML API Databricks JavaScript LLM Python RAG

O'Reilly AI & ML Books

Jan 30 - AI, Machine Learning and Computer Vision Meetup 2025-01-30 · 18:00

Register for the Zoom!

Date and Time Jan 30, 2025 at 10 AM Pacific

Swimming Upstream: Using Machine Vision to Create Sustainable Practices in Fisheries of the Future

Fishing vessels are on track to generate 10 million hours of video footage annually, creating a massive machine learning operations challenge. At AI.Fish, we are building an end-to-end system enabling non-technical users to harness AI for catch monitoring and classification both on-board and in the cloud. This talk explores our journey in building these approachable systems and working toward answering an old question: How many fish are in the ocean?

About the Speaker

Orvis Evans is a Software Engineer at AI.Fish, where he co-architects ML-Ops pipelines and develops intuitive interfaces that make machine vision accessible to non-technical users. Drawing from his background in building interactive systems, he builds front-end applications and APIs that enable fisheries to process thousands of hours of footage without machine learning expertise.

Scaling Semantic Segmentation with Blender

Generating datasets for semantic segmentation can be time-intensive. Learn how to use Blender’s Python API to create diverse and realistic synthetic data with automated labels, saving time and improving model performance. Preview the topics to be discussed in this Medium post.

About the Speaker Vincent Vandenbussche has a PhD in Physics, is an author, and Machine Learning Engineer with 10 years of experience in software engineering and machine learning.

WACV 2025 - Elderly Action Recognition Challenge

Join us for a quick update on the Elderly Action Recognition (EAR) Challenge, part of the Computer Vision for Smalls (CV4Smalls) Workshop at the WACV 2025 conference!

This challenge focuses on advancing research in Activity of Daily Living (ADL) recognition, particularly within the elderly population, a domain with profound societal implications. Participants will employ transfer learning techniques with any architecture or model they want to use. For example, starting with a general human action recognition benchmark and fine-tuning models on a subset of data tailored to elderly-specific activities.

Sign up for the EAR challenge and learn more.

About the Speaker

Paula Ramos, PhD is a Senior DevRel and Applied AI Research Advocate at Voxel51.

Transforming Programming Ed: An AI-Powered Teaching Assistant for Scalable and Adaptive Learning

The future of education lies in personalized and scalable solutions, especially in fields like computer engineering where complex concepts often challenge students. This talk introduces Lumina (AI Teaching Assistant), a cutting-edge agentic system designed to revolutionize programming education through its innovative architecture and teaching strategies. Built using OpenAI API, LangChain, RAG, and ChromaDB, Lumina employs an agentic, multi-modal framework that dynamically integrates course materials, technical documentation, and pedagogical strategies into an adaptive knowledge-driven system. Its unique “Knowledge Components” approach decomposes programming concepts into interconnected teachable units, enabling proficiency-based learning and dynamic problem-solving guidance. Attendees will discover how Lumina’s agentic architecture enhances engagement, fosters critical thinking, and improves concept mastery, paving the way for scalable AI-driven educational solutions.

About the Speaker

Nittin Murthi Dhekshinamoorthy is a computer engineering student and researcher at the University of Illinois Urbana-Champaign with a strong focus on advancing artificial intelligence to solve real-world challenges in education and technology. He is the creator of an AI agent-based Teaching Assistant, leveraging cutting-edge frameworks to provide scalable, adaptive learning solutions, and has contributed to diverse, impactful projects, including natural language-to-SQL systems and deep learning models for clinical image segmentation.

Jan 30 - AI, Machine Learning and Computer Vision Meetup

Jan 30 - AI, Machine Learning and Computer Vision Meetup 2025-01-30 · 18:00

Register for the Zoom!

Date and Time Jan 30, 2025 at 10 AM Pacific

Swimming Upstream: Using Machine Vision to Create Sustainable Practices in Fisheries of the Future

Fishing vessels are on track to generate 10 million hours of video footage annually, creating a massive machine learning operations challenge. At AI.Fish, we are building an end-to-end system enabling non-technical users to harness AI for catch monitoring and classification both on-board and in the cloud. This talk explores our journey in building these approachable systems and working toward answering an old question: How many fish are in the ocean?

About the Speaker

Orvis Evans is a Software Engineer at AI.Fish, where he co-architects ML-Ops pipelines and develops intuitive interfaces that make machine vision accessible to non-technical users. Drawing from his background in building interactive systems, he builds front-end applications and APIs that enable fisheries to process thousands of hours of footage without machine learning expertise.

Scaling Semantic Segmentation with Blender

Generating datasets for semantic segmentation can be time-intensive. Learn how to use Blender’s Python API to create diverse and realistic synthetic data with automated labels, saving time and improving model performance. Preview the topics to be discussed in this Medium post.

About the Speaker Vincent Vandenbussche has a PhD in Physics, is an author, and Machine Learning Engineer with 10 years of experience in software engineering and machine learning.

WACV 2025 - Elderly Action Recognition Challenge

Join us for a quick update on the Elderly Action Recognition (EAR) Challenge, part of the Computer Vision for Smalls (CV4Smalls) Workshop at the WACV 2025 conference!

This challenge focuses on advancing research in Activity of Daily Living (ADL) recognition, particularly within the elderly population, a domain with profound societal implications. Participants will employ transfer learning techniques with any architecture or model they want to use. For example, starting with a general human action recognition benchmark and fine-tuning models on a subset of data tailored to elderly-specific activities.

Sign up for the EAR challenge and learn more.

About the Speaker

Paula Ramos, PhD is a Senior DevRel and Applied AI Research Advocate at Voxel51.

Transforming Programming Ed: An AI-Powered Teaching Assistant for Scalable and Adaptive Learning

The future of education lies in personalized and scalable solutions, especially in fields like computer engineering where complex concepts often challenge students. This talk introduces Lumina (AI Teaching Assistant), a cutting-edge agentic system designed to revolutionize programming education through its innovative architecture and teaching strategies. Built using OpenAI API, LangChain, RAG, and ChromaDB, Lumina employs an agentic, multi-modal framework that dynamically integrates course materials, technical documentation, and pedagogical strategies into an adaptive knowledge-driven system. Its unique “Knowledge Components” approach decomposes programming concepts into interconnected teachable units, enabling proficiency-based learning and dynamic problem-solving guidance. Attendees will discover how Lumina’s agentic architecture enhances engagement, fosters critical thinking, and improves concept mastery, paving the way for scalable AI-driven educational solutions.

About the Speaker

Nittin Murthi Dhekshinamoorthy is a computer engineering student and researcher at the University of Illinois Urbana-Champaign with a strong focus on advancing artificial intelligence to solve real-world challenges in education and technology. He is the creator of an AI agent-based Teaching Assistant, leveraging cutting-edge frameworks to provide scalable, adaptive learning solutions, and has contributed to diverse, impactful projects, including natural language-to-SQL systems and deep learning models for clinical image segmentation.

Jan 30 - AI, Machine Learning and Computer Vision Meetup

Jan 30 - AI, Machine Learning and Computer Vision Meetup 2025-01-30 · 18:00

Register for the Zoom!

Date and Time Jan 30, 2025 at 10 AM Pacific

Swimming Upstream: Using Machine Vision to Create Sustainable Practices in Fisheries of the Future

Fishing vessels are on track to generate 10 million hours of video footage annually, creating a massive machine learning operations challenge. At AI.Fish, we are building an end-to-end system enabling non-technical users to harness AI for catch monitoring and classification both on-board and in the cloud. This talk explores our journey in building these approachable systems and working toward answering an old question: How many fish are in the ocean?

About the Speaker

Orvis Evans is a Software Engineer at AI.Fish, where he co-architects ML-Ops pipelines and develops intuitive interfaces that make machine vision accessible to non-technical users. Drawing from his background in building interactive systems, he builds front-end applications and APIs that enable fisheries to process thousands of hours of footage without machine learning expertise.

Scaling Semantic Segmentation with Blender

Generating datasets for semantic segmentation can be time-intensive. Learn how to use Blender’s Python API to create diverse and realistic synthetic data with automated labels, saving time and improving model performance. Preview the topics to be discussed in this Medium post.

About the Speaker Vincent Vandenbussche has a PhD in Physics, is an author, and Machine Learning Engineer with 10 years of experience in software engineering and machine learning.

WACV 2025 - Elderly Action Recognition Challenge

Join us for a quick update on the Elderly Action Recognition (EAR) Challenge, part of the Computer Vision for Smalls (CV4Smalls) Workshop at the WACV 2025 conference!

This challenge focuses on advancing research in Activity of Daily Living (ADL) recognition, particularly within the elderly population, a domain with profound societal implications. Participants will employ transfer learning techniques with any architecture or model they want to use. For example, starting with a general human action recognition benchmark and fine-tuning models on a subset of data tailored to elderly-specific activities.

Sign up for the EAR challenge and learn more.

About the Speaker

Paula Ramos, PhD is a Senior DevRel and Applied AI Research Advocate at Voxel51.

Transforming Programming Ed: An AI-Powered Teaching Assistant for Scalable and Adaptive Learning

The future of education lies in personalized and scalable solutions, especially in fields like computer engineering where complex concepts often challenge students. This talk introduces Lumina (AI Teaching Assistant), a cutting-edge agentic system designed to revolutionize programming education through its innovative architecture and teaching strategies. Built using OpenAI API, LangChain, RAG, and ChromaDB, Lumina employs an agentic, multi-modal framework that dynamically integrates course materials, technical documentation, and pedagogical strategies into an adaptive knowledge-driven system. Its unique “Knowledge Components” approach decomposes programming concepts into interconnected teachable units, enabling proficiency-based learning and dynamic problem-solving guidance. Attendees will discover how Lumina’s agentic architecture enhances engagement, fosters critical thinking, and improves concept mastery, paving the way for scalable AI-driven educational solutions.

About the Speaker

Nittin Murthi Dhekshinamoorthy is a computer engineering student and researcher at the University of Illinois Urbana-Champaign with a strong focus on advancing artificial intelligence to solve real-world challenges in education and technology. He is the creator of an AI agent-based Teaching Assistant, leveraging cutting-edge frameworks to provide scalable, adaptive learning solutions, and has contributed to diverse, impactful projects, including natural language-to-SQL systems and deep learning models for clinical image segmentation.

Jan 30 - AI, Machine Learning and Computer Vision Meetup

GenAI Study Groups (Virtual): 7-Day Challenge for Building LLM Applications 2025-01-06 · 14:00

Important: Register on the event website to enroll the AI Study Group to start your learning journey (RSVP on meetup will NOT join).

AISG (GenAI Study Group) is a learning opportunity leveraging books, papers, free online videos (youtube, coursera, deeplearning ai, etc..). It may include guided learning materials, hands-on assignments, tutoring, tech talks and group discussions.

We are hosting a few study groups during the holiday, you can register to each of the groups:

Jan 6 \~ Jan 12: 7-Day Challenge for building LLMs application Join our study group for an exciting 7-day challenge to learn and build LLMs applications together! Each day features focused tasks, collaborative discussions, and hands-on coding exercises. By the end, you’ll have a solid foundation and even a working LLMs powered application. Let’s learn, create, and grow together!

Study program structure

Self paced learning materials with virtual meeting
Daily course (30-60 minutes): Focused on specific technologies/concepts.
Daily practice: Hands-on activities using the learned concepts.
Daily workshop (30 minutes): To share learnings, troubleshoot challenges, and foster a learning community.

Who is this study group for? Developers who wants to build LLMs powered applications.

Schedule:

Day 0: Kick off, information session, online meeting
Day 1: Introduction to Prompt Engineering and LLM Fundamentals, self paced
Day 2: Building Systems with the ChatGPT API, self paced
Day 3: Introduction to LangChain for LLM Applications , self paced
Day 4: RAG using LangChain , self paced
Day 5: Functions, Tools, and Agents with LangChain, self paced
Day 6: Use Case Tutorial and Demonstration, Online meeting
Day 7: Conclusion, Final Project, Demo/showcase, Online meeting *

Venue: virtual at Discord.

GenAI Study Groups (Virtual): 7-Day Challenge for Building LLM Applications

GenAI Study Groups (Virtual): 7-Day Challenge for Building LLM Applications 2025-01-06 · 09:00

Important: Register on the event website to enroll the AI Study Group to start your learning journey (RSVP on meetup will NOT join).

AISG (GenAI Study Group) is a learning opportunity leveraging books, papers, free online videos (youtube, coursera, deeplearning ai, etc..). It may include guided learning materials, hands-on assignments, tutoring, tech talks and group discussions.

We are hosting a few study groups during the holiday, you can register to each of the groups:

Jan 6 \~ Jan 12: 7-Day Challenge for building LLMs application Join our study group for an exciting 7-day challenge to learn and build LLMs applications together! Each day features focused tasks, collaborative discussions, and hands-on coding exercises. By the end, you’ll have a solid foundation and even a working LLMs powered application. Let’s learn, create, and grow together!

Study program structure

Self paced learning materials with virtual meeting
Daily course (30-60 minutes): Focused on specific technologies/concepts.
Daily practice: Hands-on activities using the learned concepts.
Daily workshop (30 minutes): To share learnings, troubleshoot challenges, and foster a learning community.

Who is this study group for? Developers who wants to build LLMs powered applications.

Schedule:

Day 0: Kick off, information session, online meeting
Day 1: Introduction to Prompt Engineering and LLM Fundamentals, self paced
Day 2: Building Systems with the ChatGPT API, self paced
Day 3: Introduction to LangChain for LLM Applications , self paced
Day 4: RAG using LangChain , self paced
Day 5: Functions, Tools, and Agents with LangChain, self paced
Day 6: Use Case Tutorial and Demonstration, Online meeting
Day 7: Conclusion, Final Project, Demo/showcase, Online meeting *

Venue: virtual at Discord.

GenAI Study Groups (Virtual): 7-Day Challenge for Building LLM Applications

Agentic RAG with Langchain 2024-09-10 · 17:00

RAG (Retrieval Augmented Generation) is the most common approach used to get LLMs to answer questions grounded in a particular domain's data. Learn how to use Langchain, the most popular Python package for LLM orchestration, to build a RAG application with Python and Azure OpenAI. Discover how to use agentic flows in Langchain to build more complex RAG workflows.

Presented by Marlene Mhangami, Developer Advocate for Python

** Part of RAGHack, a free global hackathon to develop RAG applications. Join at https://aka.ms/raghack **

📌 Check out the RAGHack 2024 series here!

Pre-requisites: - Read the official rules and join the hack at https://aka.ms/raghack. No Purchase Necessary. Must be 18+ to enter. Contest ends 9/16/24.

Want more hands-on RAG training? Visit the Reactor series home page to see all the RAGHack 2024 sessions!

Agentic RAG with Langchain

Agentic RAG with Langchain 2024-09-10 · 17:00

RAG (Retrieval Augmented Generation) is the most common approach used to get LLMs to answer questions grounded in a particular domain's data. Learn how to use Langchain, the most popular Python package for LLM orchestration, to build a RAG application with Python and Azure OpenAI. Discover how to use agentic flows in Langchain to build more complex RAG workflows.

Presented by Marlene Mhangami, Developer Advocate for Python

** Part of RAGHack, a free global hackathon to develop RAG applications. Join at https://aka.ms/raghack **

📌 Check out the RAGHack 2024 series here!

Pre-requisites: - Read the official rules and join the hack at https://aka.ms/raghack. No Purchase Necessary. Must be 18+ to enter. Contest ends 9/16/24.

Want more hands-on RAG training? Visit the Reactor series home page to see all the RAGHack 2024 sessions!

Agentic RAG with Langchain

Agentic RAG with Langchain 2024-09-10 · 17:00

RAG (Retrieval Augmented Generation) is the most common approach used to get LLMs to answer questions grounded in a particular domain's data. Learn how to use Langchain, the most popular Python package for LLM orchestration, to build a RAG application with Python and Azure OpenAI. Discover how to use agentic flows in Langchain to build more complex RAG workflows.

Presented by Marlene Mhangami, Developer Advocate for Python

** Part of RAGHack, a free global hackathon to develop RAG applications. Join at https://aka.ms/raghack **

📌 Check out the RAGHack 2024 series here!

Pre-requisites: - Read the official rules and join the hack at https://aka.ms/raghack. No Purchase Necessary. Must be 18+ to enter. Contest ends 9/16/24.

Want more hands-on RAG training? Visit the Reactor series home page to see all the RAGHack 2024 sessions!

Agentic RAG with Langchain

An Evolving DAG for the LLM world - Julia Schottenstein of LangChain at Small Data SF 2024-01-01

Directed Acyclic Graphs (DAGs) are the foundation of most orchestration frameworks. But what happens when you allow an LLM to act as the router? Acyclic graphs now become cyclic, which means you have to design for the challenges resulting from all this extra power. We'll cover the ins and outs of agentic applications and how to best use them in your work as a data practitioner or developer building today.

➡️ Follow Us LinkedIn: https://www.linkedin.com/company/small-data-sf/ X/Twitter : https://twitter.com/smalldatasf Website: https://www.smalldatasf.com/

Discover LangChain, the open-source framework for building powerful agentic systems. Learn how to augment LLMs with your private data, moving beyond their training cutoffs. We'll break down how LangChain uses "chains," which are essentially Directed Acyclic Graphs (DAGs) similar to data pipelines you might recognize from dbt. This structure is perfect for common patterns like Retrieval Augmented Generation (RAG), where you orchestrate steps to fetch context from a vector database and feed it to an LLM to generate an informed response, much like preparing data for analysis.

Dive into the world of AI agents, where the LLM itself determines the application's control flow. Unlike a predefined DAG, this allows for dynamic, cyclic graphs where an agent can iterate and improve its response based on previous attempts. We'll explore the core challenges in building reliable agents: effective planning and reflection, managing shared memory across multiple agents in a cognitive architecture, and ensuring reliability against task ambiguity. Understand the critical trade-offs between the dependability of static chains and the flexibility of dynamic LLM agents.

Introducing LangGraph, a framework designed to solve the agent reliability problem by balancing agent control with agency. Through a live demo in LangGraph Studio, see how to build complex AI applications using a cyclic graph. We'll demonstrate how a router agent can delegate tasks, execute a research plan with multiple steps, and use cycles to iterate on a problem. You'll also see how human-in-the-loop intervention can steer the agent for improved performance, a critical feature for building robust and observable agentic systems.

Explore some of the most exciting AI agents in production today. See how Roblox uses an AI assistant to generate virtual worlds from a prompt, how TripAdvisor’s agent acts as a personal travel concierge to create custom itineraries, and how Replit’s coding agent automates code generation and pull requests. These real-world examples showcase the practical power of moving from simple DAGs to dynamic, cyclic graphs for solving complex, agentic problems.

AI/ML dbt LLM RAG Vector DB

Small Data SF 2024

Video

talk-data.com

Companies (1 result)

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