Introducing Model Registry
MLflow Model Registry is a module in MLflow that comprises a centralized store for Models, an API allowing the management of the life cycle of a model in a registry.
A typical workflow for a machine learning model developer is to acquire training data; clean, process, and train models; and from there on, hand over to a system or person that deploys the models. In very small settings, where you have one person responsible for this function, it is quite trivial. Challenges and friction start to arise when the variety and quantity of models in a team start to scale. A selection of common friction points raised by machine learning developers with regards to storing and retrieving models follows:
- Collaboration in larger teams
- Phasing out stale models in production
- The provenance of a model
- A lack of documentation for models
- Identifying the correct version of a model
- How to integrate the model with deployment tools
The main...
