With the fast pace of innovation and the release of Large Language Models like PaLM2 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 regarding data privacy and AI bias. Furthermore, Hannes will dive deep into how you can hone your machine-learning knowledge for future projects.

Hannes will discuss the challenges and ethical concerns of using advanced language models via APIs. Topics such as data privacy and AI bias will be addressed, and how machine learning engineers can mitigate these risks in your machine learning projects.