Coffee Sessions #83

Better Use cases for Text Embeddings

Text embeddings are very popular, but there are plenty of reasons to be concerned about their applications. There's algorithmic fairness, compute requirements as well as issues with datasets that they're typically trained on. In this session, Vincent gives an overview of some of these properties while also talking about an underappreciated use-case for the embeddings: labeling! 

Take-aways

ML systems can still totally fail. Maybe we should be less optimistic about 'em.

In this episode

Vincent Warmerdam

Vincent Warmerdam

Research Advocate, Rasa

Vincent D. Warmerdam is a senior data professional who worked as an engineer, researcher, team lead, and educator in the past. He's especially interested in understanding algorithmic systems so that one may prevent failure. As such, he has a preference for simpler solutions that scale, as opposed to the latest and greatest from the hype cycle. He currently works as a Research Advocate at Rasa where he collaborates with the research team to explain and understand conversational systems better. Outside of Rasa, Vincent is also well known for his open-source projects (scikit-lego, human-learn, doubtlab, and more), collaborations with open source projects like spaCy, his blog over at koaning.io, and his calm code educational project.

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Demetrios Brinkmann

Demetrios Brinkmann

Host

Demetrios is one of the main organizers of the MLOps community and currently resides in a small town outside Frankfurt, Germany. He is an avid traveller who taught English as a second language to see the world and learn about new cultures. Demetrios fell into the Machine Learning Operations world, and since, has interviewed the leading names around MLOps, Data Science, and ML. Since diving into the nitty-gritty of Machine Learning Operations he felt a strong calling to explore the ethical issues surrounding ML. When he is not conducting interviews you can find him making stone stacking with his daughter in the woods or playing the ukulele by the campfire.

Skylar Payne

Skylar Payne

Host

Data is a superpower, and Skylar has been passionate about applying it to solve important problems across society. For several years, Skylar worked on large-scale, personalized search and recommendation at LinkedIn -- leading teams to make step-function improvements in our machine learning systems to help people find the best-fit role. Since then, he shifted my focus to applying machine learning to mental health care to ensure the best access and quality for all. To decompress from his workaholism, Skylar loves lifting weights, writing music, and hanging out at the beach!