Experiment tracking in machine learning is an essential step in ML development. It helps data scientists store and manage all experiment metadata in one place, including model hyperparameters, performance measurements, run logs, and model artifacts. In the past, experiment logging was a time-consuming and error-prone process. But now, there are various tools available to automate this process and make it more efficient.
MLFlow is an open-source platform that manages the machine learning lifecycle. It allows data scientists to package their ML code in a reusable form and track experiments to compare parameters and results. Weights & Biases is an MLOps platform that offers experiment tracking, dataset versioning, and model management. Comet is another platform that helps manage, visualize, and optimize machine learning models. It allows users to track code, hyperparameters, metrics, and more. Arize AI is a machine learning observability platform that helps ML teams monitor and troubleshoot model performance in production.
Neptune AI is a platform for managing and recording ML model-building metadata. It provides features like charts, model versions, data versions, and more. Sacred is a free tool for experimenting with machine learning and provides a web-based user interface called Omniboard to visualize experiment measurements and logs. TensorBoard is a graphical toolbox for TensorFlow that allows users to inspect model graphs, track experiment metrics, and more.
Guild AI is a system for tracking ML experiments and comes with various tools for comparing experiments. Polyaxon is a platform for scalable and repeatable ML applications that offers experiment tracking and other features. ClearML is an open-source platform supported by Allegro AI that streamlines the machine learning process. Valohai is an MLOps platform that provides experiment tracking, version control, and other capabilities. Pachyderm is an open-source data science platform that allows users to control the entire ML cycle.
With these experiment-tracking tools, data scientists can automate and streamline their ML development process, save time, and improve model performance.