Abstract: Techniques are disclosed for facilitating the tuning of hyperparameter values during the development of machine learning (ML) models using visual analytics in a data science platform. In an example embodiment, a computer-implemented data science platform is configured to generate, and display to a user, interactive visualizations that dynamically change in response to user interaction. Using the introduced technique, a user can, for example, 1) tune hyperparameters through an iterative process using visual analytics to gain and use insights into how certain hyperparameters affect model performance and convergence, 2) leverage automation and recommendations along this process to optimize the tuning given available resources, 3) collaborate with peers, and 4) view costs associated with executing experiments during the tuning process.

Abstract: Techniques are disclosed for facilitating the tuning of hyperparameter values during the development of machine learning (ML) models using visual analytics in a data science platform. In an example embodiment, a computer-implemented data science platform is configured to generate, and display to a user, interactive visualizations that dynamically change in response to user interaction. Using the introduced technique, a user can, for example, 1) tune hyperparameters through an iterative process using visual analytics to gain and use insights into how certain hyperparameters affect model performance and convergence, 2) leverage automation and recommendations along this process to optimize the tuning given available resources, 3) collaborate with peers, and 4) view costs associated with executing experiments during the tuning process.