The Best AI Hands-On Training Sessions to Boost Your Career at ODSC Europe 2024

From our last blog, you know that hands-on training is an essential part of your development as a data and AI practitioner. In this blog, you’ll learn about the skills and techniques you can learn at ODSC Europe through its wide range of expert-led sessions.

Reinforcement Learning for Finance

Dr. Yves J. Hilpisch | CEO | The Python Quants, The AI Machine

In this workshop, you’ll explore how Reinforcement Learning and related algorithms, such as Deep Q-Learning (DQL), can be beneficially applied to typical problems in finance, such as algorithmic trading, dynamic hedging of options, or dynamic asset allocation. In particular, you’ll discuss the problem of limited data availability in finance and solutions to it, such as synthetic data generation through GANs, and how to apply the DQL algorithm to typical financial problems.

How to Make LLMs Fit into Commodity Hardware Again: A Practical Guide

Oliver Zeigermann | Machine Learning Engineer | Techniker Krankenkasse

Christian Hidber, PhD | Consultant | bSquare

This hands-on workshop explores how to bring LLMs down from the cloud and run on machines that you manage. You’ll delve into different approaches for making LLMs fit onto affordable GPUs (like a T4) or — in special cases — even make them run on CPU, as well as discuss how to evaluate and compare the performance of these small LLMs.

LLM Application from Inception to Production

Leonardo De Marchi | VP of Labs | Thomson Reuters

This workshop is designed to explore how artificial intelligence can be used to generate creative outputs and to inspire technical audiences to use their skills in new and creative ways. This session will also include a series of code exercises designed to give participants hands-on experience working with AI models to generate creative outputs.

Exercises include:

• Generating text using NLP models like LSTM and Transformer.
• Advanced strategies to leverage Open Source LLMS
• Alignment
• Fine-tuning an open-source model
• Evaluating LLMs outputs automatically
• Using Gen AI in different fields, like computer vision and audio
• Using reinforcement learning to generate creative outputs that match certain criteria or goals

Designing AI Architectures with Domain-Driven Design: A Use Case-Centric Approach

Shawn Kyzer | Associate Director, Data Engineering, Research & Early Science | AstraZeneca

Join this session for a fresh perspective on tackling the challenge of designing architectures that effectively align with business objectives and deliver lasting value by leveraging the power of Domain-Driven Design (DDD) in the context of AI system development.

Through a use case-centric approach, you will explore how DDD principles can be applied to create modular, scalable, and maintainable AI architectures, delve into key concepts such as bounded contexts, microservices, and tactical patterns, and demonstrate their practical application in designing AI systems.

How to run scalable, fault-tolerant RAG with a vector database

JP Hwang | Technical Curriculum Developer | Weaviate

The journey from prototyping RAG apps to production is anything but easy with many traps and pitfalls. In this workshop, you’ll get to see first-hand some of the potential risks, and how the right infrastructure tool can help you to mitigate them.

You will also get hands-on with an AI-native, vector database, implement features like quantization, multi-tenancy, replication, and horizontal scaling, and experience how big a difference each of these can make to the RAG system’s performance.

By the end, you’ll understand some of the key considerations in selecting your infrastructure tools, and what to consider when going to production.

Develop LLM Powered Applications with LangChain and LangGraph

Eden Marco | LLM Specialist | Google

In this engaging and intensive hands-on workshop, you’ll learn how to unleash the power of LLM agents using the LangGraph library by LangChain. You’ll dive deep into the advanced capabilities of LangGraph, exploring its integration with LangChain to create robust, efficient, and versatile LLM solutions. You’ll also get a comprehensive introduction to key components such as LCEL, multi-agents, reflection agents, Reflexion agents, and more. Participants will also get hands-on experience with advanced RAG architectures.

Going From Unstructured Data to Vector Similarity Search

Steven Pousty, PhD | Principal and Founder | Tech Raven Consulting

One of the key concepts used in AI modeling is the storage and query of vectors. Join this workshop for 2 examples of unstructured data, images, and journal abstracts. You will then work this data all the way through to a usable data store with an application on top of it. You’ll cover transformers,

Forecasting with Sktime — Introduction and Advanced Features: Pipelines, Hierarchical and Probabilistic Forecasts, Deep Learning and Foundation Models

Franz Kiraly, PhD | Founder, Core Developer | sktime

This workshop gives a hands-on introduction to forecasting with sktime, the most widely used scikit-learn compatible framework library for learning with time series, including advanced features such as hierarchical and probabilistic forecasts, and an overview of different model categories, including classical statistical, ML, deep learning, and foundation models.

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Originally posted on OpenDataScience.com

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