"Once I have my training data set, I can an equal number of various classes, like do I have equal numbers of males and females or do I have equal numbers of other kinds of classes, and we have a set of several metrics that you can use for the statistical analysis so you get real insight into easier data set balance," Saha explained. He says that it is designed to analyze the data for bias before you start data prep, so you can find these kinds of problems before you even start building your model. And what that does is it allows you to have insight into your data and models throughout your machine learning lifecycle," Bratin Saha, Amazon VP and general manager of machine learning told TechCrunch. "We are launching Amazon SageMaker Clarify. Slow startup, it will break your workflow if every time you start the machine, it takes 5 minutes. SageMaker instances are currently 40 more expensive than their EC2 equivalent. To review, open the file in an editor that reveals hidden Unicode characters. See why Amazon SageMaker is the most cost-effective choice for end-to-end machine learning support and scalability. This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. Clarify aims to provide bias detection across the machine learning workflow, enabling developers to build greater fairness and transparency into their ML models. For example, Sagemaker Clarify is their bias. For deployed models, Clarify monitors for changes in feature importance and issues alerts. Today at AWS re:Invent, AWS introduced Amazon SageMaker Clarify to help reduce bias in machine learning models. SageMaker Clarify may be one of the most important features being debuted by AWS this week considering ongoing events. Clarify can be applied to the broad range of models trained by SageMaker’s customers. As companies rely increasingly on machine learning models to run their businesses, it's imperative to include anti-bias measures to ensure these models are not making false or misleading assumptions. Amazon SageMaker Clarify Processing Use SageMaker Clarify to create a processing job for the detecting bias and explaining model predictions with feature attributions.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |