![]() The solutions are fully customizable and showcase the use of AWS CloudFormation templates and reference architectures so you can accelerate your ML journey. To make it easier to get started, SageMaker JumpStart provides a set of solutions for the most common use cases that can be deployed readily with just a few clicks. Amazon SageMaker JumpStart: Amazon SageMaker JumpStart helps you quickly and easily get started with machine learning. In addition, Ground Truth offers automatic data labeling which uses a machine learning model to label your data. As part of the workflows, labelers have access to assistive labeling features such as automatic 3D cuboid snapping, removal of distortion in 2D images, and auto-segment tools to reduce the time required to label datasets. These workflows support a variety of use cases including 3D point clouds, video, images, and text. Get started with labeling your data in minutes through the SageMaker Ground Truth console using custom or built-in data labeling workflows. Amazon SageMaker Ground Truth: Amazon SageMaker Ground Truth is a fully managed data labeling service that makes it easy to build highly accurate training datasets for machine learning. You then can directly deploy the model to production with just one click, or iterate on the recommended solutions with Amazon SageMaker Studio to further improve the model quality. SageMaker Autopilot will automatically explore different solutions to find the best model. With SageMaker Autopilot, you simply provide a tabular dataset and select the target column to predict, which can be a number (such as a house price, called regression), or a category (such as spam/not spam, called classification). Amazon SageMaker Autopilot: Amazon SageMaker Autopilot eliminates the heavy lifting of building ML models, and helps you automatically build, train, and tune the best ML model based on your data. Accelerate innovation with purpose-built tools for every step of ML development, including labeling, data preparation, feature engineering, statistical bias detection, auto-ML, training, tuning, hosting, explainability, monitoring, and workflows. Their definitions, see Amazon SageMaker Clarify Terms for Bias andįor additional information about post-training bias metrics, see Learn How Amazon SageMaker Clarify Helps Detect Bias and Fairness Measures for Machine Learning in Finance.Amazon SageMaker Studio: Amazon SageMaker helps data scientists and developers to prepare, build, train, and deploy high-quality machine learning (ML) models quickly by bringing together a broad set of capabilities purpose-built for ML. Not be due to a machine learning model, but might still be detectable by post-trainingĪmazon SageMaker Clarify tries to ensure a consistent use of terminology. ![]() Group, referred to as a less favored facet d, experiences anĪdverse effect even when the approach taken appears to be fair. For example, the US concept of disparate impact that occurs when a There are legal concepts of fairness that might not be easy to capture because theyĪre hard to detect. Requiring different bias metrics to measure. There are different notions of fairness, each You assess performance by analyzing predicted labels or byĬomparing the predictions with the observed target values in the data with respect to These analyses take into consideration the data, including the labels, and ![]() Post-training bias analysis can help reveal biases that might have emanated fromīiases in the data, or from biases introduced by the classification and predictionĪlgorithms.
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