mlflow

You can track machine learning experiment parameters and metrics by logging them within an MLflow run. This allows you to record parameters like learning rate, capture evaluation metrics, and save output artifacts to ensure reproducibility across different training runs.

The best way to manage the ML lifecycle is using a framework that handles experiment tracking, model registry, and deployment together. This provides version control for models and ensures experiments can be reproduced reliably before moving to production.

Yes, you can use MLflow for model registry and version control with PyTorch or TensorFlow. It supports logging models from these frameworks, allowing you to version, stage, and manage them for production deployment.

To log a learning rate parameter in an MLflow run, you initiate a new run and record the parameter with a specific value such as 0.01. This logs the parameter alongside metrics and artifacts for that specific experiment execution.

Yes, MLflow supports deploying Scikit-Learn models as a REST API endpoint. It provides deployment tools that take registered models and serve them, enabling data science teams to move from tracking to production deployment.