How can I use MLflow for tracking my machine learning experiments?

How can I use MLflow for tracking my machine learning experiments?

You can use MLflow to log parameters, metrics, and artifacts from your experiments. Start by installing MLflow and then use its API to log your data.

What are the main components of MLflow?

MLflow has four main components: Tracking, Projects, Models, and Registry. Tracking is for logging experiments, Projects for packaging code, Models for managing models, and Registry for versioning.

Can I integrate MLflow with other tools?

Yes, MLflow can be integrated with various tools like TensorFlow, PyTorch, and Scikit-learn, as well as cloud platforms like AWS and Azure.

Is MLflow open-source?

Yes, MLflow is an open-source platform, and you can find its source code on GitHub.

How do I deploy a model using MLflow?

You can deploy a model using MLflow by saving it in the MLflow Model format and then using the mlflow models serve command to create a REST API.