JupyterLab
Browser-based development environment presenting notebooks, code editors, terminals and data files in one tabbed workspace. This image variant ships a Python data-science stack preinstalled.
Browser-based development environment presenting notebooks, code editors, terminals and data files in one tabbed workspace. This image variant ships a Python data-science stack preinstalled.
Platform for tracking machine-learning experiments, logging parameters, metrics and artifacts per run, with a model registry holding versions, stage transitions and lineage back to the producing run.
A complete platform for data analysis and machine learning experimentation that combines interactive computing with experiment tracking. JupyterLab provides the familiar notebook environment for data exploration, model development, and visualization, while MLflow tracks experiments, manages model versions, and facilitates collaboration between data scientists. This combo addresses the challenge of maintaining reproducible data science workflows - ensuring that experiments can be recreated, models can be properly versioned, and insights can be shared across teams. The combination provides both the interactive development environment needed for exploration and the systematic tracking required for production ML workflows.