Speaker
Abstract
Self-hosted Language Models are going to power the next generation of applications in critical industries like financial services, healthcare, and defence. Self-hosting LLMs, as opposed to using API-based models, comes with its own host of challenges - as well as needing to solve business problems, engineers need to wrestle with the intricacies of model inference, deployment and infrastructure. In this talk we are going to discuss the best practices in model optimisation, serving and monitoring - with practical tips and real case-studies.
Interview
At TitanML our focus is on making Generative AI applications easier to develop, deploy and serve. A large focus of our work recently is making it easier to build applications that involve both RAG and JSON constrained outputs.
Almost every business is trying to build and deploy LLM applications at the moment, however very few of them have successfully got these applications into production. Our teams are experts in deploying and serving LLM apps so we have a lot of tips and tricks to help other developers avoid common pitfalls.
This session is interesting for those working with or thinking of building with Generative AI, especially self-hosted open source AI. It is not a 'code-along' session, however there may be some technical concepts.
I want this persona to realize that deploying LLMs within your own environment is a viable option and is not as scary as it might appear!
Topics
QCon London 2024 is a three day conference for senior software engineers, architects and team leads. An international program committee of working engineers selects every session. Patterns and practices, not products and pitches.
From the same track
Monday 8 April
10:35 Windsor (5th Fl.) Session AI/ML Retrieval-Augmented Generation (RAG) Patterns and Best Practices Jay Alammar Director & Engineering Fellow @Cohere & Co-Author of "Hands-On Large Language Models" The rise of LLMs that coherently use language has led to an appetite to ground the generation of these models in facts and private collections of data. 11:45 Mountbatten (6th Fl.) Session AI/ML Navigating LLM Deployment: Tips, Tricks, and Techniques Meryem Arik Co-Founder and CEO @Doubleword (Previously TitanML), Recognized as a Technology Leader in Forbes 30 Under 30, Recovering Physicist Self-hosted Language Models are going to power the next generation of applications in critical industries like financial services, healthcare, and defence. 13:35 Mountbatten (6th Fl.) Session AI/ML Reach Next-Level Autonomy with LLM-Based AI Agents Tingyi Li Enterprise Solutions Architect @AWS Generative AI has emerged rapidly since the release of ChatGPT, yet the industry is still at its very early stage with unclear prospects and potential. 14:45 Mountbatten (6th Fl.) Session AI/ML LLM and Generative AI for Sensitive Data - Navigating Security, Responsibility, and Pitfalls in Highly Regulated Industries Stefania Chaplin, Azhir Mahmood As large language models (LLM) become more prevalent in highly regulated industries, dealing with sensitive data and ensuring the security and ethical design of machine learning (ML) models is paramount. 15:55 Whittle (3rd Fl.) Session AI/ML The AI Revolution Will Not Be Monopolized: How Open-Source Beats Economies of Scale, Even for LLMs Ines Montani Co-Founder & CEO @Explosion, Core Developer of spaCy With the latest advancements in Natural Language Processing and Large Language Models (LLMs), and big companies like OpenAI dominating the space, many people wonder: Are we heading further into a black box era with larger and larger models, obscured behind APIs controlled by big… 17:05 Fleming (3rd Fl.) Session AI/ML How Green is Green: LLMs to Understand Climate Disclosure at Scale Leo Browning First ML Engineer @ClimateAligned Assessment of the validity of climate finance claims requires a system that can handle significant variation in language, format, and structure present in climate and financial reporting documentation, and knowledge of the domain-specific language of climate science and finance.