Which tool is primarily used for tracking experiments, versions, and deployments?

The tool primarily used for tracking experiments, versions, and deployments is MLflow. MLflow provides a robust framework for managing the machine learning lifecycle, which includes experimentation, reproducibility, and deployment, making it especially valuable in data science and machine learning projects.

One of MLflow's key features is its ability to log and track various parameters, metrics, and artifacts of machine learning models throughout the experimentation process. This allows data scientists to systematically record different model versions, compare results, and manage the evolution of their models effectively. Additionally, MLflow includes functionalities for packaging models and managing deployments across various environments, ensuring that the models can be reliably reproduced and utilized.

In contrast, Kubernetes is primarily an orchestration tool for deploying and managing containerized applications, rather than specifically focusing on tracking ML experiments. TensorFlow is a powerful machine learning library used for building and training models, but it does not inherently include tools for tracking experiments. Spark is a big data processing framework that is often used for large-scale data processing and analytics rather than directly for machine learning experiment tracking. Thus, MLflow stands out as the specialized tool designed specifically for experiment tracking and management in the machine learning domain.