Machine Learning Labeling - Amazon SageMaker Ground Truth - AWS

Amazon SageMaker Data Labeling enables you to build highly accurate training datasets for machine learning quickly.

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Machine Learning Labeling - Amazon SageMaker Ground Truth - AWS

Introduction

Here are the key points about Amazon SageMaker Ground Truth:

  • It's a service that allows you to create high-quality training datasets for machine learning models by leveraging human feedback.

  • It offers comprehensive human-in-the-loop capabilities across the ML lifecycle, from data generation and annotation to model review, customization and evaluation.

  • Key benefits include:

    • Getting human-generated data to customize models
    • Evaluating and comparing foundation models
    • Creating high-quality training datasets
    • Accelerating human-in-the-loop tasks

  • Major use cases:

    • Generating example/demonstration data like text summaries, Q&A pairs, etc.
    • Getting comparison and ranking data on model outputs
    • Evaluating and "red teaming" models to find vulnerabilities
    • Data labeling for text, images, video, audio, etc.

  • It's available as both a self-service offering and an AWS-managed service.

  • Customers like T-Mobile, NFL, AstraZeneca and Tyson Foods use it for various ML applications.

  • You can get started by setting up your own labeling workflow or connecting with the AWS team to offload labeling operations.

So in essence, it's a flexible service to incorporate human intelligence into the machine learning pipeline to improve model quality and performance.