🛣 Finding your path in ML with NLP Engineer Urszula Czerwinska

In this episode, I'm speaking with Urszula Czerwinska about her path as a data scientist, the projects she worked on, experiences gained as a data scientist, as well as the challenges she's overcome in bringing her machine learning (ML) into production. Join our Discord community: https://discord.gg/tEYvqxwhah --- Timestamps: 0:00 - Podcast intro 1:15 - Guest intro and how you got into data science 3:48 - Finding your fit – research or industry and when to transition 7:23 - What types of ML projects do you specialize in 10:41 - ML explainability and interpretability 15:26 - ML explainability with non-technical stakeholders 17:13 - What problems does your team solve within the organization 20:56 - ML in production – how to bring your ML projects from research to production 25:17 - The tools you can't live without 28:11 - Do you have a set process for productizing ML projects 30:08 - Team structures and communication for data science teams 33:42 - Who's in charge of setting up infrastructure for a project and job title discussion 36:29 - Interesting tools and repositories you work with 39:30 - How do you stay up to date 42:00 - Biggest challenges for you in ML 45:12 - Favorite and least favorite thing about being a data scientist 49:52 - Handling a workplace that doesn't understand what a data scientist is 53:07 - Data scientists are 🦄 53:30 Good papers you read recently 58:12 - Tips to improve the data science workflow   Relevant Links: - flair: https://github.com/flairNLP/flair - AllenNLP: https://github.com/allenai/allennlp - Papers with Code: https://paperswithcode.com/ - Dair.ai newsletter: https://dair.ai/newsletter/ - HuggingFace: https://huggingface.co/blog