MLFlow is an API-first machine learning tool, meaning it primarily offers functionality through its application programming interface. It provides a user interface where all the necessary libraries, dependencies, and supported languages are captured.
The tool enables users to reproduce experiments, log models, and allows for development and deployment in independent modular models. This means each stage of a machine learning workflow can be treated as an independent stage, offering greater flexibility and modularity.
Users do not have to use MLFlow as an end-to-end solution, but can utilize individual features as needed. This makes MLFlow a versatile tool for a variety of machine learning tasks.
MLFlow provides a user-friendly interface similar to AWS SageMaker and Azure Workstation, making it accessible for users who have experience with these platforms.
As an open-source tool, MLFlow is free to use and can be adapted or modified as needed. It consists of four distinct components, offering a variety of tools for machine learning workflows.

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