A model registry serves as a crucial infrastructure for managing different versions of machine learning models. It facilitates the tracking and retrieval of models through an API, significantly streamlining the workflow for practitioners. By using a model registry, individuals can avoid the chaos of locating stored models, thus enhancing efficiency and ensuring a more organized approach to model management in future projects.
We discussed βπ₯¦ Broccoli AIβ a couple weeks ago, which is the kind of AI that is actually good/healthy for a real world business. Bengsoon Chuah, a data scientist working in the energy sector, joins us to discuss developing and deploying NLP pipelines in that environment. We talk about good/healthy ways of introducing AI in a company that uses on-prem infrastructure, has few data science professionals, and operates in high risk environments.
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