Source: thenewstack.io
Datatron Leans in on Governance for Machine Learning ModelsCategory: Software, Business, Data, Kubernetes, Docker, Infrastructure, artificial-intelligence
From his experience working at Lyft, Snap and Twitter, https://www.linkedin.com/in/harishd/ saw a lot of friction between those who create machine learning models, the data science team, and those putting them into production, the machine learning engineering side of the organization or DevOps team. Data scientists, he said, tend to work on historical data in siloed environments, usually the confines of their laptops. But when a model goes into production, the way a model behaves with real-time https://thenewstack.io/category/data/ can be very different.
If things go wrong in the production environment, then things go wrong in a development environment,” he said.
Datatron takes the view that there’s a triumvirate involved in model management: the data science team, the operations team and the risk team.
If things go wrong in the production environment, then things go wrong in a development environment,” he said.
Datatron takes the view that there’s a triumvirate involved in model management: the data science team, the operations team and the risk team.
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