When using MLflow's code-based logging (Models from Code) for an AI agent, what is the purpose of calling `mlflow.models.set_model(agent)` in the agent source file?

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B. It declares which Python object represents the model to be invoked at serving time when the source file is executed.. `mlflow.models.set_model()` is part of MLflow's Models from Code approach. Calling it in the agent's source file declares which Python object (e.g., a LangChain chain or PyFunc class) is the model. When the agent is deployed, the source file is executed, and the object declared via `set_model()` is what gets invoked for each serving request.

When using MLflow's code-based logging (Models from Code) for an AI agent, what is the purpose of calling `mlflow.models.set_model(agent)` in the agent source file?

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When using MLflow's code-based logging (Models from Code) for an AI agent, what is the purpose of calling mlflow.models.set_model(agent) in the agent source file?

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When using MLflow's code-based logging (Models from Code) for an AI agent, what is the purpose of calling `mlflow.models.set_model(agent)` in the agent source file?