Learn About LLMs With These ODSC East 2024 Sessions
Large Language Models are everywhere these days. Stay at the forefront of increasingly ubiquitous technology with the leading AI training conference, ODSC East this April 23rd-25th in Boston. Check out some of the LLM-focused training sessions, workshops, and talks you’ll find at the conference.
NLP with GPT-4 and other LLMs: From Training to Deployment with Hugging Face and PyTorch Lightning
Dr. Jon Krohn | Chief Data Scientist | Nebula.io
Hear from one of the leading experts in Large Language Models, Dr. Jon Krohn as he takes a deep dive into the models like GPT-4 that are transforming the world in general and the field of data science in particular at an unprecedented pace.
You’ll explore the breadth of capabilities of state-of-the-art LLMs like GPT-4 can deliver through hands-on code demos that leverage the Hugging Face and PyTorch Lightning Python libraries.
Enabling Complex Reasoning and Action with ReAct, LLMs, and LangChain
Giuseppe Zappia | Principal Solutions Architect | AWS
Shelbee Eigenbrode | Principal Machine Learning Specialist Solutions Architect | AWS
Human reasoning meets Large Language Models in ReAct, which combines a mimic of human chain of thought processes combined and the ability to engage with an external environment to solve problems and reduce the likelihood of hallucinations and reasoning errors.
In this workshop, you will learn how to employ the ReAct technique to allow an LLM to determine where to find information to service different types of user queries, using LangChain to orchestrate the process. You’ll see how it uses Retrieval Augmented Generation (RAG) to answer questions based on external data, as well as other tools for performing more specialized tasks to enrich the output of your LLM.
EVENT — ODSC East 2024
In-Person and Virtual Conference
April 23rd to 25th, 2024
Join us for a deep dive into the latest data science and AI trends, tools, and techniques, from LLMs to data analytics and from machine learning to responsible AI.
Ben Needs a Friend — An intro to building Large Language Model applications
Benjamin Batorsky, PhD | Data Science Consultant
Calling all introverts! Ditch all your tedious social plans and learn how to make your own AI friend powered by Large Language Models in this tutorial from Benjamin Batrosky.
In this session, you’ll cover some of the essential topics in LLM development, including prompt engineering and fine-tuning, document embeddings, Retrieval-Augmented Generation (RAG), and LangChain and Transformers libraries. By the end of the tutorial, you’ll have a basic familiarity with how to use the latest tools for LLM development.
Data Synthesis, Augmentation, and NLP Insights with LLMs
Tamilla Triantoro, PhD | Associate Professor, Business Analytics | Quinnipiac University
Build essential skills for the fields of social media analysis, customer behavior studies, content generation and more, in this workshop on generating realistic and functional synthetic data using Large Language Models. You’ll also explore methods for enriching this data and making it more applicable for real-world scenarios, as well as applying NLP techniques to synthesized and augmented data to uncover patterns, sentiments, and trends.
Building Using Llama 2
Amit Sangani | Director of Partner Engineering | Meta
Join this session to build a basic understanding of Llama 2 models, how to access and use them to build core components of the AI chatbot using LangChain and Tools. You’ll explore core concepts around Prompt Engineering and Fine-Tuning and programmatically implement them using Responsible AI principles in this hands-on session.
LLM Best Practises: Training, Fine-Tuning and Cutting Edge Tricks from Research
Sanyam Bhutani | Sr. Data Scientist and Kaggle Grandmaster
Explore the power of fine-tuning models in this East 2024 workshop, which will take you through the tips and tricks of creating and fine-tuning Large Language Models. You will also implement cutting-edge ideas of building these systems from the best research papers.
Starting with the foundations behind what makes a LLM before moving into fine-tuning our own GPT and finally implementing some of the cutting edge tricks of building these models.
LLMs Meet Google Cloud: A New Frontier in Big Data Analytics
Mohammad Soltanieh-ha, PhD | Clinical Assistant Professor | Boston University
Dive into the world of cloud computing and big data analytics with Google Cloud’s advanced tools and big data capabilities. Designed for industry professionals eager to master cloud-based big data tools, this workshop offers hands-on experience with various big data analytics tools, such as Dataproc, BigQuery, Cloud Storage, and Compute Engine. You will also dive into the new LLM capabilities of Google Cloud, with an exploration of how these innovative AI models can extract deeper insights, generate creative text, and automate large-scale tasks, taking your big data analysis to the next level.
LangChain on Kubernetes: Cloud-Native LLM Deployment Made Easy & Efficient
Ezequiel Lanza | AI Open source Evangelist | Intel
This session will illustrate how the open source framework, LangChain, can simplify the process of creating generative AI interfaces. You’ll walk through the process of smoothly and efficiently transitioning your trained models to working applications by deploying an end-to-end LLM containerized application built with LangChain in a cloud-native environment using open-source tools like Kubernetes, LangServe, and FastAPI.
Training an OpenAI Quality Text Embedding Model from Scratch
Andriy Mulyar | Founder & CTO | Nomic AI
Get past the barriers in this talk on Nomic AI trained nomic-embed-text-v1, the first fully auditable open-data, open-weights and open-training code text embedding model that outperforms the performance of OpenAI Ada-002. You will learn how text embedding models are trained, the various training decisions that impact model capabilities and tips for successfully using them in your production applications.
Tracing In LLM Applications
Amber Roberts | ML Growth Lead | Arize AI
In this session, you’ll focus on best practices for tracing calls in a given Large Language Model application by providing the terminology, skills, and knowledge needed to dissect various span kinds.
Informed by working with dozens of enterprises with LLM apps in production and research on what works, attendees can learn span types and how to view traces from an LLM callback system and establish troubleshooting workflows to break down each call an application is making to an LLM. The session will explain and dive into both top-down workflows and bottom-up workflows.
Reasoning in Large Language Models
Maryam Fazel-Zarandi, PhD | Researcher Engineering Manager, FAIR | Meta
Recent work has shown that prompting or fine-tuning LLMs to generate step-by-step rationales, or asking them to verify their final answer can lead to improvements on reasoning tasks. While these methods have proven successful in specific domains, there is still no general framework for LLMs to be capable of reasoning in a wide range of situations. In this talk, you will explore some of the existing methods used for improving and eliciting reasoning in large language models, methods for evaluating reasoning in these models, and discuss limitations and challenges.
Let’s learn about LLMs!
As you can see, there’s plenty of choice in terms of what direction you want to take when it comes to training your LLM. But if you want to keep up on the latest in large language models, and not be left in the dust, then you don’t want to miss the NLP & LLM track as part of ODSC East this April.
Connect with some of the most innovative people and ideas in the world of data science, while learning first-hand from core practitioners and contributors. Learn about the latest advancements and trends in NLP & LLMs, including pre-trained models, with use cases focusing on deep learning, training and finetuning, speech-to-text, and semantic search.
Confirmed sessions include, with many more to come:
- NLP with GPT-4 and other LLMs: From Training to Deployment with Hugging Face and PyTorch Lightning
- Enabling Complex Reasoning and Action with ReAct, LLMs, and LangChain
- Ben Needs a Friend — An intro to building Large Language Model applications
- Data Synthesis, Augmentation, and NLP Insights with LLMs
- Building Using Llama 2
- Quick Start Guide to Large Language Models
- LLM Best Practises: Training, Fine-Tuning and Cutting Edge Tricks from Research
- LLMs Meet Google Cloud: A New Frontier in Big Data Analytics
- Operationalizing Local LLMs Responsibly for MLOps
- LangChain on Kubernetes: Cloud-Native LLM Deployment Made Easy & Efficient
- Tracing In LLM Applications
Originally posted on OpenDataScience.com
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