Our Schedule

Week 1 - LLMS

Wed 15 Jan - Thu 16 Jan

This track will cover the latest topics, tools, and advancements in Large language Models (LLMs). This track delves into the transformative power of using LLMs with real-time data retrieval, enhancing the accuracy and relevance of AI-generated responses. Engage in our hands-on workshops demonstrating the latest LLMs techniques and their applications. Hear and learn from leading experts and LLM practitioners on how to make these systems more reliable and effective.

Week 2 - RAG

Wed Jan 22 - Thu Jan 23

Ready to take your Retrieval Augmented Generation (RAG) implementation to the next level? These hands-on workshops dive deep into the architecture, tools, and strategies needed to scale your RAG systems without compromising on performance or maintainability.

Cameron Pfiffer - Developer Relations Engineer at .txt
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Extracting structured data from unstructured text can be challenging. This workshop shows how to combine RAG with structured generation to turn messy review text into clean, structured data that can be queried and analyzed systematically.We'll build a pipeline to answer natural-language queries like "What do users say about battery life?" or "What are common complaints?" We'll first apply semantic search methods to locate relevant reviews, then apply structured generation to reliably extract structured data points like review categories, reported issues, and sentiment. We'll demonstrate how structured generation achieves this reliability by constraining LLM outputs to exact schemas with the Outlines library. You'll also learn how to extract multiple data points in a single LLM call for reliable and cost-effective analysis.We'll walk through- Building tools to find relevant reviews using vector embeddings and semantic search- Implementing structured generation with Outlines to extract specific data points- Applying structured data analysis problemsThe goal of the course is to demonstrate how to turn unstructured data into reliable, structured data that you can actually use. We'll provide example code and schemas that you can adapt for your own projects. Basic Python and LLM experience recommended, but not required.
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Session Outline:

Module 1: Foundations of Structured Generation
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Learn how to make language models produce reliable, structured data using the Outlines library. We'll motivate this module by demonstrating some common failure modes when working with LLMs, and how to address them with open-source tools.
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Module 2: Building RAG Systems with Structured Outputs
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In this module, we will demonstrate how to efficiently search for relevant documents and produce structured output from unstructured text. At the end of this module, you will have a simple structured data + RAG system to play with.
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Module 3: Working with Structured Results
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Finally, we provide a simple demonstration of how to analyze structured results -- what can you do with structured data? How can we build a quantitative understanding of our unstructured data? Expect to run some simple statistical analyses and visualizations.
All lessons include hands-on practice with real datasets, complete Jupyter notebooks, and example code you can adapt for your own projects. Python experience recommended.
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Learning Objectives:
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After this workshop, you will be able to:
- Understand the basics of structured generation, RAG, and simple data analysis.- Optimize structured generation calls through prompting and structure design.- Create a simple system to answer natural-language queries about unstructured data.
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Tools:
- Python
- Outlines (structured generation library)
- A vector database like Milvus or ChromaDB
- Pandas (for data manipulation)- Jupyter Notebooks
- Example datasets and starter code (provided via GitHub)

Stefan Webb - Developer Advocate at Zilliz
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Retrieval-augmented generation (RAG) has become increasingly popular in the latest wave of Generative AI applications, from sophisticated question-answering systems to advanced semantic search engines. This has led to an bewildering explosion of RAG methodology and research. How should we design optimal RAG systems given the breadth of design choices available?In this workshop, we demonstrate how to quantify the performance of retrieval and generation in your RAG system using only open-source tools such as LangChain, HuggingFace, Milvus and RAGAS. Attendees will learn how to implement a complete RAG evaluation pipeline to explore design choices in a principled way, and leave with practical code examples and best practices that can be applied in real-world scenarios.We finish with a summary of recent research in RAG evaluation, explaining improvements in RAG benchmarks, fine-tuned judge models, and methodology that extends LLM-as-a-Judge. Participants will leave not only well equipped with the skills to build optimal RAG systems today but be early adopters of cutting edge methodology and build pipelines that will stay relevant in the rapidly developing field of Generative AI.
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Module 1: Why is RAG evaluation important and how is it done? (30 minutes)
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In the first module, we’ll discuss why RAG evaluation is necessary to building a state-of-the-art application and some challenges in doing so. We will introduce the LLM-as-a-Judge method and explain how
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Module 2: Let’s Build a Basic RAG with Milvus and LangChain. (30 minutes)
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In the second module, participants will build a basic RAG pipeline using only open-source frameworks - Milvus, LangChain, and the HuggingFace libraries, models, and datasets - with a Jupyter notebook. This provides the foundation for testing and evaluating RAG design choices.
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Module 3: RAGAS and RAG Evaluation (45 minutes)
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In this module, we demonstrate how to use the RAGAS library to calculate performance metrics on both retrieval and generation and show how to use them to make design decisions. We frame the discussion using actual RAG methods that are used in production.
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Module 4: Future Directions in RAG Evaluation (15 minutes)
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In this module, we briefly summarize some of the latest research in RAG evaluation, including methodology that builds upon LLM-as-a-Judge. Attendees will leave with a sense of how RAG evaluation may differ in 12 months.
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By the end of this session, participants will be able to:
- Set up and configure LangChain and Milvus for RAG evaluation workflows
- Implement both task-based and introspection-based evaluation strategies
- Use RAGAS to assess context relevance, answer faithfulness, and retrieval quality
- Deploy LLM-as-Judge effectively while mitigating common biases
- Apply industry-standard metrics for measuring RAG performance in production

Week 3 - AI Agents

Wed Jan 29 - Thu jan 30

At the ODSC AI Agents track attendees will gain comprehensive insights into the latest advancements in Agentic AI techniques. They will learn about the architecture and deployment of AI agents, explore real-world applications, and understand how these agents can drive efficiency and innovation in various sectors. Sessions will cover practical implementation strategies, best practices for integrating AI agents into existing systems, and emerging trends in the field. Participants will leave equipped with the knowledge and tools to leverage AI agents to solve complex problems.

Week 4 - AI Builders

Wed Feb 5 - Thu Feb 6

These workshop are all about bringing awesome AI application to life—rapidly build AI-driven solutions like chatbots, AI agents, and RAG systems in real time. It’s not just about learning tools; it’s about applying them to solve problems, create value, and explore what’s possible in AI today. Whether you’re an experienced practitioner or new to AI, this session is designed to inspire creativity and empower you to build.

A Few Of Our Speakers

Stefan Webb

Developer Advocate at Zilliz

Chloe Leung

ML Solutions Architect at Predibase

Devvret Rishi

CEO at Predibase

Laurie Voss

VP Developer Relations at LlamaIndex

Niels Bantilan

Chief ML Engineer at Union.AI

Julien Simon

Chief Evangelist at Arcee.ai

JP Hwang

Technical Curriculum Developer at Weaviate

Edward Donner

Co-founder and CTO at Nebula.io

Chris Alexiuk

Head of LLMs at AI Makerspace | Ox

Greg Loughnane

Co-Founder & CEO at AI Makerspace

Valentina Alto

Azure Specialist at Microsoft

Ivan Lee

CEO / Founderat Datasaur

Sinan Ozdemir

AI & LLM Expert & CTO at LoopGenius

Dr. Andre Franca

CTO at Ergodic

Suman Debnath

Principal AI/ML Advocate | Amazon Web Services

Mastering AI Agents, LLMs, RAG, and Reasoning

A 4-week Virtual Training SummitJan 15 - Feb 6

Start 2025 Strong: Master AI Agents, LLMs, RAGand Reasoning in just 4 Weeks

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Our Schedule

Week 1 - LLMS

Wed 15 Jan - Thu 16 Jan

Week 2 - RAG

Wed Jan 22 - Thu Jan 23

Week 3 - AI Agents

Wed Jan 29 - Thu jan 30

Week 4 - AI Builders

Wed Feb 5 - Thu Feb 6

Our Schedule

A Few Of Our Speakers

Stefan Webb

Developer Advocate at Zilliz

Chloe Leung

ML Solutions Architect at Predibase

Devvret Rishi

CEO at Predibase

Laurie Voss

VP Developer Relations at LlamaIndex

Niels Bantilan

Chief ML Engineer at Union.AI

Julien Simon

Chief Evangelist at Arcee.ai

JP Hwang

Technical Curriculum Developer at Weaviate

Edward Donner

Co-founder and CTO at Nebula.io

Chris Alexiuk

Head of LLMs at AI Makerspace | Ox

Greg Loughnane

Co-Founder & CEO at AI Makerspace

Valentina Alto

Azure Specialist at Microsoft

Ivan Lee

CEO / Founderat Datasaur

Sinan Ozdemir

AI & LLM Expert & CTO at LoopGenius

Dr. Andre Franca

CTO at Ergodic

Suman Debnath

Principal AI/ML Advocate | Amazon Web Services

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Mastering AI Agents, LLMs, RAG,
and Reasoning

A 4-week Virtual Training Summit
Jan 15 - Feb 6

Start 2025 Strong: Master AI Agents, LLMs, RAG
and Reasoning in just 4 Weeks