Data Democratization Podcast: Stories about AI, Data, and Privacy 46. How to (finally) move your AI products to production
Oct 19, 2023
Wolfgang Weidinger, an expert in AI and Analytics at Generali Insurance and chairman of the Vienna Data Science Group, offers valuable insights into moving AI from development to production. He discusses selecting appropriate tools, the importance of soft skills, and the challenges organizations face during implementation. Wolfgang also highlights the significance of community in data science, shares his experiences in the insurance industry, and emphasizes the need for collaboration and effective communication to ensure success.
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AI Production Is Multifaceted
- Moving AI projects into production involves handling engineering, legal, and operational challenges beyond model development.
- The machine learning model is just a small part of a complex ecosystem needed for deployment.
Plan Skills and Infrastructure
- Build awareness and conduct capability gap analysis to align data science, engineering, and IT skills.
- Avoid expecting a single data scientist with a notebook to deploy AI at scale without team and infrastructure support.
AI Adoption Focus in Insurance
- Insurance AI adoption focuses more on optimization and augmentation than full automation.
- Customers prioritize efficient claim processing over the technology specifics behind it.