Step By Step Guide To Building ML Model Registry

Model Registry in MLOps offers a collaborative hub to work together on various stages of the ML lifecycle including managing multiple model artefacts, tracking and governing models. Additionally, it is a place to find, publish, and use ML models. With a centralized tracking system for trained ML models, the registry stores model lineage, versioning, and additional configuration information. Model registry key features: Central Repository: It allows the user to manage all their models in one place and share the information with team members.Model Versioning: Helps in automatically keeping track of the versions, including data usage, hyperparameters, or the user's created model. The registry notifies the user for every event in the ML lifecycle. Source: neptune.ai User-f