Document and save a model at different stages of its learning lifecycle to back-test effectiveness. With our Testing Studio, you can easily store and manage models and their corresponding datasets. Track the performance of your models over time, making informed decisions about which models to deploy in specific production environments.

You can also group and categorize models by their relevance to a particular family of data or project line, making it easy to find the right model for the job, and benefit from the AI assisted insights it can generate.

In addition to optimizing model performance, our Testing Studio allows you to compare and contrast different models against each other. You can see which model performs best for specific datasets and adjust your models accordingly.

A/B test various models against holdout data and back-test training path effectiveness by saving and duplicating models at different learning stages.

Our intuitive interface allows you to review the importance of features, trust the outputs of models, and better understand the decisions made by the system. You can view the ranking of algorithms, analyze feature importance, and evaluate easy to understand model performance metrics.

The Testing Studio also enables you to explain model outputs and understand the reasons for their predictions. With the ability to visualize and interact with your data, you can make informed decisions and optimize your data science process for maximum efficiency.