AWS SAGEMAKER CLARIFY CODE
“Once the practitioner selects a data transformation, Amazon SageMaker Studio Notebook generates the corresponding code within the notebook so it can be repeatedly applied every time the notebook is run,” the company said. The new feature allows data scientists to visually review data characteristics and remediate data quality problems, the company said, adding that the tool automatically generates charts to help users identify data-quality issues and suggests data transformations to help fix common problems. Amazon SageMaker Studio Notebook is now updatedĪlong with adding governance features to SageMaker, AWS has added new capabilities to Amazon SageMaker Studio Notebook to help enterprise data science teams collaborate and prepare data faster within the notebook.Ī data preparation capability within Amazon SageMaker Studio Notebook will now help data science teams identify errors in data sets and correct them from inside the notebook. “Practitioners can also include additional information using a self-guided questionnaire to document model information (e.g., performance goals, risk rating), training and evaluation results (e.g., bias or accuracy measurements), and observations for future reference to further improve governance and support the responsible use of ML,” AWS said.įurther, the company has added Amazon SageMaker Model Dashboard to provide a central interface within SageMaker to track machine learning models.įrom the dashboard, enterprise can also use built-in integrations with Amazon SageMaker Model Monitor (model and data drift monitoring capability) and Amazon SageMaker Clarify (ML bias-detection capability), the company said, adding that the end-to-end visibility will help streamline machine learning governance.
The tool provides a single location to store model information in the AWS console and it can auto-populate training details like input data sets, training environment, and training results directly into Amazon SageMaker Model Cards, the company said.
AWS SAGEMAKER CLARIFY MANUAL
The tool then automatically creates access policies with necessary permissions within minutes, the company said.ĪWS has also added a new tool to SageMaker called Amazon SageMaker Model Cards to help data science teams shift from manual recordkeeping. With the new tool, administrators can select and edit prebuilt templates based on various user roles and responsibilities. To tackle these challenges, the cloud services provider has added Amazon SageMaker Role Manager to make it easier for administrators to control access and define permission for users. This can become complex as the size of machine learning teams increases within an enterprise, AWS said in a statement.Īnother challenge is to monitor the deployed models for bias and ensure they are performing as expected, the company said. Data engineering and machine learning teams currently use spreadsheets or ad hoc lists to navigate access policies needed for all processes involved.
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