Release Notes v7.44

What's new?

• Users in AWS deployments can create SageMaker autopilot models in the advanced analytics zone of Ursa Studio. To create an autopilot model, users pick an existing object for training and validation. They also select the relevant independent variable fields, as well as the target field. Users must also select a problem type, such as regression, and an objective, such as MSE, from the available options, depending on the nature of the target field to be predicted. Users can then kick off a training of the model. During training, AWS SageMaker will test 10 different models against the training data, and will select the model which performed the best on the chosen objective. Full details about model performance and validation can be found in SageMaker Studio. Once an autopilot model is trained, it can be hooked up to a Bespoke Model object and run per the normal conventions of those objects. In order to unlock this feature in client deployments, two new environment variables must be added to Ursa Studio Fargate task definition, CLIENT_TAG and SAGEMAKER_IAM_ROLE_ARN. Some SageMaker resources must also be spun up, for which we can provide a CDK script.

What improvements have been made to existing features?

• Allow collapse of blocks of patterns. If all the blocks are collapsed, then dragging a block will move the entire block as a unit, unlike the current uncollapsed behavior of only moving the individual pattern.
• Allow a filter to be applied to semantic mapping objects. This option allows users to work with a partition of the upstream source data table. With each filter, users can choose any field from the source data table. The filtering will be performed on the raw, untrimmed values of the source data.