What is the role of ML Engineers in the time of GPT4 and BARD? // Hannes Hapke // LLMs in Prod Con

// Abstract
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.

// Bio
Hannes was the first ML engineer at Digits, where he built the MLOPs foundation for their ML team. His interest in production machine learning ranges from building ML pipelines to scaling similarity-based ML to process millions of banking transactions daily.

Prior to Digits, Hannes implemented ML solutions for a number of applications, incl. retail, health care, or ERP companies. He co-author two machine learning books:
* Building Machine Learning Pipeline (O'Reilly)
* NLP in Action (Manning)

[bart]

Very cool! It's nice to hear some good points, from someone from the inside of your own industry, instead of all that noise we have in the media.

[VikashKumarCurious]

Great video! thanks for sharing such concise and to the point informations. 
Just a thought "Feedback loops- Incorrect GPT4 generations" could be added as few shot examples in prompt. Isn't it. 15:45