What are the four key components of MLflow?

MLflow is an open-source platform aimed at managing the ML lifecycle, which includes several key components. The correct answer identifies these components accurately as Tracking, Projects, Models, and Model Registry.

Tracking allows users to log and query experiments, making it easier to keep track of metrics and parameters that can influence model performance. This is essential for evaluating different runs and understanding which configurations work best.

Projects provide a standardized way of packaging data science code in a reusable and reproducible form. By using projects, data scientists can share their work with others and ensure that the environment setup is consistent across different systems.

Models represent the actual algorithms that have been trained on the data. This component allows for both saving and loading models and supports a variety of formats, which is crucial for deploying models into production.

Model Registry serves as a centralized place to manage the lifecycle of a machine learning model. It enables users to track model versions, model stages (like staging, production, or archived), and manage permissions. This organization is vital for teams collaborating on model development and deployment.

While other options include relevant terms like Data Pipeline, Experimentation, and Deployment, they do not encompass the four essential components of MLflow as clearly as the chosen answer does. This precise categorization of