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.

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

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.

Despite their advanced capabilities, Language Model Models (LLMs) are often too slow and resource-intensive for use at scale in voice applications, particularly for large-scale audio or low-latency real-time processing. SlimFast addresses this challenge by introducing Domain Specific Language Models (DSLMs) that are distilled from LLMs on specific data domains and tasks. SlimFast provides a practical solution for real-world applications, offering blazingly fast and resource-conscious models while maintaining high performance on speech intelligence tasks. We demo a new ASR-DSLM pipeline that we recently built, which performs summarization on call center audio.

Adept AI is developing a natural language software collaborator that utilizes LLMs to perform software tasks described in natural language. However, LLMs can suffer from overconfidence, hallucinations, and a lack of self-awareness, which can lead to incorrect actions. Jacob highlights an example of how the model can make a wrong action and emphasizes the importance of implementing safety checks such as action reversibility and content filters. By incorporating safety checks, Adept AI aims to improve the model's capabilities and ensure that it moves in the right direction.

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.

Large Language Models (LLM’s) are starting to revolutionize how users can search for, interact with, and generate new content. There is one challenge though: how do users easily apply LLM’s to their own data? LLM’s are pre-trained with enormous amounts of publicly available natural language data, but they don’t inherently know about your personal/organizational data. LlamaIndex solves this by providing a central data interface for your LLM’s. In this talk, we talk about the tools that LlamaIndex offers (both simple and advanced) to ingest and index your data for LLM use.