Skip to main contentExperiment tracking is a crucial aspect of machine learning and data science workflows, offering several key advantages. First, it ensures reproducibility by automatically logging the code, datasets, parameters, and results associated with each experiment, making it easy to revisit and replicate past results. It also helps streamline collaboration among teams by providing a centralized record of all experiments, reducing the risk of redundant work and facilitating knowledge sharing. Moreover, experiment tracking enables better model management, allowing users to compare different models and tune hyperparameters effectively, ultimately leading to more efficient model optimization.
ClearML
ClearML simplifies experiment tracking by providing an integrated platform that automatically captures and logs all aspects of a machine learning pipeline. With ClearML, users can track their experiments effortlessly, from data preprocessing to model training and evaluation, all within a unified interface. It supports version control of datasets, models, and code, ensuring that each experiment can be fully reproduced. ClearML also offers real-time monitoring, visualization tools for metrics, and the ability to compare experiment results, helping teams accelerate the development and deployment of machine learning models. By automating the tracking process, ClearML reduces the overhead of manual logging and fosters a more organized and efficient workflow.
We provide below screenshots of the tool and some of experiments output. The tool is typically deployed in docker containers locally to the site that the application will be hosted. It can be orchestrated to do finetuning with other datasets to address model drift.
