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.

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.