Verta Experiment Management helps you build high-quality models and release them to production faster, using state-of-the-art experiment tracking, model reproducibility, dataset versioning, and model meta-data visualization capabilities. Ensure model reproducibility and quality from experiment to production.
Where does Experiment Management fit into the ML Lifecycle?
Building an ML model involves substantial trial and error and iterating on hundreds to thousands of models. Tracking and comparing each of these experiments are essential to debug the performance of different models versions and determine the final release-ready model.
What are the benefits of experiment management?
It helps track and visualize hundreds and thousands of experiments and quickly identify the high-performing model. You can organize all your experiments in a unified view, manage inputs and outputs of the modeling process such as metrics, observations, attributes to filter and compare various experiment runs.
It ensures model reproducibility. You can obtain full model reproducibility using 4 key ingredients - code, data, configuration, and environment variable.
It allows you to get better visibility and collaborate with your team. With features like interactive charts, custom dashboards, and inline comments you can easily share your experiment results and get real-time feedback from your peers.