How can you effectively manage and improve the entire machine learning (ML) lifecycle, which includes activities like experiment tracking and model production monitoring? How can you efficiently handle models throughout the entire ML lifecycle, from tracking experiments to monitoring models in production? By adding just one line of code to your notebook or script, you can begin tracking your experiments. This functionality is compatible with any machine learning library and can be used for any ML task, regardless of where you run your code.
