As the landscape of large language models (LLMs) advances at an unprecedented rate, novel techniques are constantly emerging to make LLMs faster, safer, and more reliable in production. This talk explores some of the latest patterns that builders have adopted when integrating LLMs into their products.

Adam will highlight potential negative user outcomes that can arise when adding LLM-driven capabilities to an existing product. He will also discuss strategies and best practices that can be used to ensure a high-quality user experience for customers.

Scalable, Serverless deployments of LangChain apps on the cloud without sacrificing the ease and convenience of local development. Streaming experiences without worrying about infrastructure

With the fast pace of innovation and the release of Large Language Models like Bard or GPT4, the role of data scientists and machine learning engineers is rapidly changing. APIs from Google, OpenAI, and other companies democratize access to machine learning but also commoditize some machine learning projects. In his talk, Hannes will explain the state of the ML world and which machine learning projects are in danger of being replaced by 3rd party APIs. He will walk the audience through a framework to determine if an API could replace your current machine-learning project and how to evaluate Machine Learning APIs in terms of data privacy and AI bias. Furthermore, Hannes will dive deep into how you can hone your machine-learning knowledge for future projects.

One of the biggest challenges of getting LLMs in production is their sheer size and computational complexity. This talk explores how smaller specialised models can be used in most cases to produce equally good results while being significantly cheaper and easier to deploy.

LLMs unlock a huge range of new product possibilities but with everyone using the same base models, how can you build something differentiated? In this talk, we'll look at case studies of companies that have and haven't got it right and draw lessons for what you can do.

Today, our personal and professional networks have reached an unprecedented level of complexity. The last two decades of tech innovation have connected us to more people, across more communication channels, spanning a wider range of contexts than ever before. Our growing networks have become intractable to manage as individuals, let alone teams.

This panel discussion is centered around a crucial topic in the tech industry - data privacy and security in the context of large language models and AI systems. The discussion highlights several key themes, such as the significance of trust in AI systems, the potential risks of hallucinations, and the differences between low and high-affordability use cases. The discussion promises to be thought-provoking and informative, shedding light on the latest developments and concerns in the field. We can expect to gain valuable insights into an issue that is becoming increasingly relevant in our digital world.