Amazon SageMaker Canvas is a no-code workspace that enables analysts and citizen data scientists to generate accurate ML predictions for their business needs. Starting today, Canvas supports advanced model build configurations such as selecting training method (Ensemble/Hyper parameter optimization) and algorithms, customizing training/validation data split ratio, and setting limits on autoML iterations and job run time, thus allowing users to customize model building configurations without having to write a single line of code. This flexibility can provide more robust and insightful model development. Non-technical stakeholders can use the no-code features with default settings, while citizen data scientists can experiment with various ML algorithms and techniques, helping them understand which methods work best for their data and optimize to ensure the model's quality and performance.

In addition to model building configurations, SageMaker Canvas now also provides a model leaderboard. A leaderboard allows you to easily compare key performance metrics (e.g., accuracy, precision, recall, F1 score) for different models configurations evaluated by Canvas in order to generate the best model for your data, thereby improving transparency into model building and enabling you to make informed decisions on model choices. You can also view the entire model building workflow including suggested preprocessing step, algorithm, and hyperparameter ranges in a notebook.

To avail these functionalities, log out and log back in to SageMaker Canvas and click on ‘Configure model’ when building models. The ability to customize model configurations, view model leaderboard and download autoML workflow notebook in Amazon SageMaker Canvas is now available in all AWS regions where SageMaker Canvas is supported.