ML and AI may sound sexy to investors, but if you work in the field you've probably spent late nights reviewing outputs manually, poured over logs and ran root cause analyses until your eyes hurt. If you've created data products at a company where analytics and data science held no meaning before your arrival, you've probably spent many-a-late-night explaining the basics of data collection, why ETL cannot be half-baked and that when you create a supervised model it needs to be supervised. Companies hoping to create a data product can have a data scientist show them how ML/AI can further their product, help them scale, or create better recommendations than their competitors. What companies are not always aware of is once the algorithm is created the data scientist is usually handicapped until more data-hires are made to build the necessary pipelines and frontend to put the algorithm in production. With the number of unique data-titles growing each year, how should the first data-evangelist-wrangler-wizard navigate title assignment?
In this episode
Elizabeth is a researcher turned data nerd. With a background in social and clinical sciences Elizabeth is focused on developing data solutions that focus on creating value adds while allowing the user to make more intelligent decisions.
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.