Summary Building a machine learning model can be difficult, but that is only half of the battle. Having a perfect model is only useful if you are able to get it into production. In this episode Stepan Pushkarev, founder of Hydrosphere, explains why deploying and maintaining machine learning projects in production is different from regular software projects and the challenges that they bring. He also describes the Hydrosphere platform, and how the different components work together to manage the full machine learning lifecycle of model deployment and retraining. This was a useful conversation to get a better understanding of the unique difficulties that exist for machine learning projects.
Announcements
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Interview
Introduction How did you get involved in the area of data management? Can you start by explaining what Hydrosphere is and share its origin story? In your experience, what are the most challenging or complicated aspects of managing machine learning models in a production context?
How does it differ from deployment and maintenance