MCQ IN COMPUTER SCIENCE & ENGINEERING

Detailed explanation-1: -Pattern learning automatically analyzes your data and surfaces textual constraints that may apply to your dataset.

Detailed explanation-2: -Amazon Clarify detects and measures potential bias using a variety of metrics so that they can address potential bias, and explain model predictions. Amazon SageMaker Clarify can detect potential bias during data preparation, after model training, and in your deployed model.

Detailed explanation-3: -SageMaker includes the following new features for re:Invent 2022. Build, train, and deploy ML models using geospatial data. Document information about your ML models in a single place for streamlined governance and reporting throughout the ML lifecycle. A pre-built, visual overview of all the models in your account.

Detailed explanation-4: -ML governance with Amazon SageMaker uses SageMaker Role Manager, SageMaker Model Cards, and SageMaker Model Dashboard to help you simplify access control and enhance transparency over your ML projects.

Detailed explanation-5: -SageMaker Studio This is an integrated machine learning environment that helps you to build, train, deploy, and analyze the models all in the same application. SageMaker Canvas This is an auto ML service that provides people with no coding background or experience, who can build models and make predictions with them.