AI Practitioner

Managing and Monitoring Deep Learning/Machine Learning Experiments with MLflow What is MLflow? MLflow is an open-source platform for managing the complete machine learning lifecycle, including training, evaluation, and deployment of models. During the development of complex models, experiments are repeatedly conducted with changing hyperparameters, data versions, source code, and model architectures. Without proper tracking, it becomes difficult to reproduce results or improve model performance. MLflow solves these issues with the following four key components: MLflow Tracking : Stores and compares metadata such as parameters, metrics, models, and logs MLflow Projects : Defines code and execution environments for reproducibility MLflow Models : A universal format to save and deploy models trained with various frameworks MLflow Model Registry : Supports model versioning, approval, and stage transitions like Production and Staging W...