MLFlow Components and Functionality:

Tracking Server: Captures parameters (inputs), metrics (results), artifacts (code and data), and metadata (information about inputs, results, and where they are recorded).
Models: Represents the best chosen model or models for execution.
MLFlow Architecture:

Consists of a user interface (UI) and the backend MLFlow server. Backend contains different layers storing metadata and artifacts.
Metadata is stored in either SQL databases or local folders. Artifacts, representing code and data, are always placed in folders.
Local vs. Remote Storage Options:

On a local machine, all metadata and artifacts are saved in local folders. However, when deployed elsewhere, databases can be used for metadata, and local folders or cloud storages (like S3) can be used for artifacts.
Reproducibility in MLFlow:

MLFlow tracks all model runs, ensuring reproducibility. Even with non-deterministic models (like neural networks), once the best model is chosen, its results can be reproduced since the model's parameters are fixed.
Real-World Implementation:

In scenarios with non-deterministic algorithms, while initial results may vary, MLFlow tracks details of all runs. Once the best model is identified, it can be deployed and will produce consistent results due to its fixed parameters.

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