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What is Encord?

High-quality data is essential for optimizing model performance to its fullest potential. You can generate and oversee training data tailored for various visual modalities. By troubleshooting models, enhancing performance, and personalizing foundational models, you can elevate your work. Implementing expert review, quality assurance, and quality control workflows enables you to provide superior datasets for your AI teams, leading to increased model efficacy. Encord's Python SDK facilitates the integration of your data and models while enabling the creation of automated pipelines for the training of machine learning models. Additionally, enhancing model precision involves detecting biases and inaccuracies in your data, labels, and models, ensuring that every aspect of your training process is refined and effective. By focusing on these improvements, you can significantly advance the overall quality of your AI initiatives.

What is Amazon SageMaker Ground Truth?

Amazon SageMaker offers a suite of tools designed for the identification and organization of diverse raw data types such as images, text, and videos, enabling users to apply significant labels and generate synthetic labeled data that is vital for creating robust training datasets for machine learning (ML) initiatives. The platform encompasses two main solutions: Amazon SageMaker Ground Truth Plus and Amazon SageMaker Ground Truth, both of which allow users to either engage expert teams to oversee the data labeling tasks or manage their own workflows independently. For users who prefer to retain oversight of their data labeling efforts, SageMaker Ground Truth serves as a user-friendly service that streamlines the labeling process and facilitates the involvement of human annotators from platforms like Amazon Mechanical Turk, in addition to third-party services or in-house staff. This flexibility not only boosts the efficiency of the data preparation stage but also significantly enhances the quality of the outputs, which are essential for the successful implementation of machine learning projects. Ultimately, the capabilities of Amazon SageMaker significantly reduce the barriers to effective data labeling and management, making it a valuable asset for those engaged in the data-driven landscape of AI development.

Media

Media

Integrations Supported

Amazon SageMaker
Amazon SageMaker Unified Studio
Cogito
Python
ZenML

Integrations Supported

Amazon SageMaker
Amazon SageMaker Unified Studio
Cogito
Python
ZenML

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

$0.08 per month
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Encord

Company Location

United Kingdom

Company Website

encord.com

Company Facts

Organization Name

Amazon Web Services

Date Founded

2006

Company Location

United States

Company Website

aws.amazon.com/es/sagemaker/data-labeling/

Categories and Features

Data Labeling

Human-in-the-loop
Labeling Automation
Labeling Quality
Performance Tracking
Polygon, Rectangle, Line, Point
SDK
Supports Audio Files
Task Management
Team Collaboration
Training Data Management

Categories and Features

Data Labeling

Human-in-the-loop
Labeling Automation
Labeling Quality
Performance Tracking
Polygon, Rectangle, Line, Point
SDK
Supports Audio Files
Task Management
Team Collaboration
Training Data Management

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