Which open source software does NeMo integrate with for managing the ML development lifecycle?

NeMo integrates with MLFlow for managing the machine learning development lifecycle. MLFlow is specifically designed to help data scientists and machine learning engineers manage and streamline various aspects of the ML workflow, including experimentation, reproducibility, and deployment. By integrating with MLFlow, NeMo can leverage its capabilities for tracking experiments, logging parameters, and managing model versions, which enhances the overall workflow for building and deploying AI models.

In contrast, while TensorFlow and Keras are powerful frameworks for building neural networks, they do not provide the comprehensive management functionalities required for the entire ML lifecycle as MLFlow does. PyTorch is also primarily a deep learning framework similar to TensorFlow but lacks the ML lifecycle management tools integrated directly within it or as part of its core package. Therefore, MLFlow stands out as the best choice for managing the ML development lifecycle within the NeMo framework.